From 9ed413353d48cc0d235b0dd89b201d5da098c751 Mon Sep 17 00:00:00 2001 From: Garvan Date: Thu, 21 Nov 2024 12:42:01 -0500 Subject: [PATCH 1/2] Updates + Merge --- .github/pull_request_template.md | 39 +++ CONTRIBUTING.md | 111 ++++++++ README.md | 72 ++++- src/sqlite/README.md | 90 +++--- src/sqlite/pyproject.toml | 3 +- src/sqlite/src/sqlite/__init__.py | 13 +- src/sqlite/src/sqlite/server.py | 454 ++++++++++++++++++++++++------ src/sqlite/uv.lock | 191 ++++++++----- 8 files changed, 756 insertions(+), 217 deletions(-) create mode 100644 .github/pull_request_template.md create mode 100644 CONTRIBUTING.md diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..f6d5e1a8 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,39 @@ + + +## Description + +## Server Details + +- Server: +- Changes to: + +## Motivation and Context + + +## How Has This Been Tested? + + +## Breaking Changes + + +## Types of changes + +- [ ] New MCP Server +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Breaking change (fix or feature that would cause existing functionality to change) +- [ ] Documentation update + +## Checklist + +- [ ] I have read the [MCP Protocol Documentation](https://modelcontextprotocol.io) +- [ ] My server follows MCP security best practices +- [ ] I have updated the server's README accordingly +- [ ] I have tested this with an LLM client +- [ ] My code follows the repository's style guidelines +- [ ] New and existing tests pass locally +- [ ] I have added appropriate error handling +- [ ] I have documented all environment variables and configuration options + +## Additional context + diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..c76c0687 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,111 @@ +# Contributing to MCP Servers + +Thank you for your interest in contributing to the Model Context Protocol (MCP) servers! This document provides guidelines and instructions for contributing. + +## Types of Contributions + +### 1. New Servers +Adding new servers is one of the most valuable ways to contribute. Before creating a new server: + +- Check the [modelcontextprotocol.io](https://modelcontextprotocol.io) documentation +- Ensure your server doesn't duplicate existing functionality +- Consider whether your server would be generally useful to others +- Follow security best practices from the MCP documentation + +### 2. Improvements to Existing Servers +Enhancements to existing servers are welcome! This includes: + +- Bug fixes +- Performance improvements +- New features +- Documentation improvements +- Security enhancements + +### 3. Documentation +Documentation improvements are always welcome: + +- Fixing typos or unclear instructions +- Adding examples +- Improving setup instructions +- Adding troubleshooting guides + +## Getting Started + +1. Fork the repository +2. Clone your fork: + ```bash + git clone https://github.com/your-username/mcp-servers.git + ``` +3. Add the upstream remote: + ```bash + git remote add upstream https://github.com/modelcontextprotocol/servers.git + ``` +4. Create a branch: + ```bash + git checkout -b my-feature + ``` + +## Development Guidelines + +### Code Style +- Follow the existing code style in the repository +- Use TypeScript for JavaScript/Node.js servers +- Include appropriate type definitions +- Add comments for complex logic + +### Documentation +- Include a detailed README.md in your server directory +- Document all configuration options +- Provide setup instructions +- Include usage examples + +### Testing +- Add appropriate tests for new functionality +- Ensure existing tests pass +- Test your changes thoroughly + +### Security +- Follow security best practices +- Implement proper input validation +- Handle errors appropriately +- Document security considerations + +## Submitting Changes + +1. Commit your changes: + ```bash + git add . + git commit -m "Description of changes" + ``` +2. Push to your fork: + ```bash + git push origin my-feature + ``` +3. Create a Pull Request through GitHub + +### Pull Request Guidelines + +- Fill out the pull request template completely +- Link any related issues +- Provide clear description of changes +- Include any necessary documentation updates +- Add screenshots for UI changes +- List any breaking changes + +## Review Process + +1. Maintainers will review your PR +2. Address any requested changes +3. Once approved, your PR will be merged + +## Community + +- Participate in [GitHub Discussions](https://github.com/modelcontextprotocol/servers/discussions) +- Follow the [Code of Conduct](CODE_OF_CONDUCT.md) + +## Questions? + +- Check the [documentation](https://modelcontextprotocol.io) +- Ask in GitHub Discussions + +Thank you for contributing to MCP Servers! \ No newline at end of file diff --git a/README.md b/README.md index 782a8278..dc5fc6e9 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,73 @@ # MCP servers ![NPM Version](https://img.shields.io/npm/v/%40modelcontextprotocol%2Fexample-servers) -Example servers for the Model Context Protocol, to demonstrate the kinds of things you can do! +[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) -## Getting started +A collection of reference implementations and community-contributed servers for the Model Context Protocol (MCP). This repository showcases the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to external tools and data sources. -Install from npm: +## 🌟 Featured Servers -```sh +- **[Filesystem](src/filesystem)** - Secure file operations with configurable access controls +- **[GitHub](src/github)** - Repository management, file operations, and GitHub API integration +- **[Google Drive](src/gdrive)** - File access and search capabilities for Google Drive +- **[PostgreSQL](src/postgres)** - Read-only database access with schema inspection +- **[Slack](src/slack)** - Channel management and messaging capabilities +- **[Memory](src/memory)** - Knowledge graph-based persistent memory system +- **[Puppeteer](src/puppeteer)** - Browser automation and web scraping +- **[Brave Search](src/brave-search)** - Web and local search using Brave's API +- **[Google Maps](src/google-maps)** - Location services, directions, and place details + +## 🚀 Getting Started + +### Installation + +```bash +# Install all servers globally npm install -g @modelcontextprotocol/servers + +# Or install individual servers +npm install -g @modelcontextprotocol/server-github +npm install -g @modelcontextprotocol/server-filesystem +# etc... ``` -Then, the servers will be globally available on your PATH: +### Usage + +Each server can be run directly from the command line: -```sh -mcp-server-everything -mcp-server-postgres -# ... +```bash +mcp-server-github +mcp-server-filesystem ~/allowed/path +mcp-server-postgres "postgresql://localhost/mydb" ``` -Each server will operate differently. See the READMEs within [src](src/) for more information. \ No newline at end of file +## 🛠️ Creating Your Own Server + +Interested in creating your own MCP server? Visit the official documentation at [modelcontextprotocol.io/introduction](https://modelcontextprotocol.io/introduction) for comprehensive guides, best practices, and technical details on implementing MCP servers. + +## 🤝 Contributing + +See [CONTRIBUTING.md](CONTRIBUTING.md) for information about contributing to the MCP servers repository. + +## 🔒 Security + +See [SECURITY.md](SECURITY.md) for reporting security vulnerabilities. + +## 📜 License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +## 💬 Community + +- [Discord](https://discord.gg/modelcontextprotocol) +- [GitHub Discussions](https://github.com/modelcontextprotocol/servers/discussions) + +## ⭐ Support + +If you find MCP servers useful, please consider: +- Starring the repository +- Contributing new servers or improvements +- Sharing your experience with the community + +--- + +Managed by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements! diff --git a/src/sqlite/README.md b/src/sqlite/README.md index 849bfbff..36b1958b 100644 --- a/src/sqlite/README.md +++ b/src/sqlite/README.md @@ -1,62 +1,48 @@ -# Simple SQLite Notes Server +# SQLite MCP Server -A basic MCP server implementation that demonstrates note-taking functionality using the three core MCP primitives: Resources, Prompts, and Tools. +## Overview +A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through SQLite. This server enables running SQL queries, analyzing business data, and automatically generating business insight memos that can be enhanced with Claude's analysis when an Anthropic API key is provided. -## Core Concepts +## Components ### Resources -Resources are how clients access data from the server. In this case, they're notes stored in SQLite. - -```python -# Access notes through a custom URI scheme -note:///example_note # Gets the content of 'example_note' -``` +The server exposes a single dynamic resource: +- `memo://insights`: A continuously updated business insights memo that aggregates discovered insights during analysis + - Auto-updates as new insights are discovered via the append-insight tool + - Optional enhancement through Claude for professional formatting (requires Anthropic API key) ### Prompts -Prompts allow generating text based on server state. Our server has a note summarization prompt that can be styled. - -```python -# Example prompt with style argument -{ - "name": "summarize-notes", - "arguments": { - "style": "academic" # Can be any style descriptor - } -} -``` +The server provides a demonstration prompt: +- `mcp-demo`: Interactive prompt that guides users through database operations + - Required argument: `topic` - The business domain to analyze + - Generates appropriate database schemas and sample data + - Guides users through analysis and insight generation + - Integrates with the business insights memo ### Tools -Tools modify server state. We have a simple tool to add new notes. - -```python -# Adding a new note -{ - "name": "add-note", - "arguments": { - "name": "my_note", - "content": "This is my note content" - } -} -``` +The server offers six core tools: -## Key Implementation Details - -### Handler Registration -All decorated handlers must be inside `__init__`: -```python -def __init__(self): - super().__init__("sqlite") - - @self.list_resources() - async def handle_list_resources(): - # Handler code here - - @self.read_resource() - async def handle_read_resource(): - # Handler code here -``` +#### Query Tools +- `read-query`: Execute SELECT queries on the database +- `write-query`: Execute INSERT, UPDATE, or DELETE queries +- `create-table`: Create new database tables + +#### Schema Tools +- `list-tables`: Get a list of all tables in the database +- `describe-table`: View the schema of a specific table -### Storage -- Uses SQLite for persistent storage -- Helper methods handle database operations -- Clients are notified of state changes \ No newline at end of file +#### Analysis Tools +- `append-insight`: Add new business insights to the memo resource + +## Installation + +```bash +# Required: Python 3.10+ +python -m pip install mcp-sqlite-server + +# Run with default settings +mcp-sqlite-server + +# Run with custom database and Anthropic integration +mcp-sqlite-server --db-path ~/analysis.db --anthropic-key sk-xxx +``` diff --git a/src/sqlite/pyproject.toml b/src/sqlite/pyproject.toml index 7e8b79b0..3934f119 100644 --- a/src/sqlite/pyproject.toml +++ b/src/sqlite/pyproject.toml @@ -5,7 +5,8 @@ description = "A simple SQLite MCP server" readme = "README.md" requires-python = ">=3.11" dependencies = [ - "mcp>=0.9.0", + "mcp>=0.9.1", + "anthropic>=0.39.0", ] [build-system] diff --git a/src/sqlite/src/sqlite/__init__.py b/src/sqlite/src/sqlite/__init__.py index 8fc09807..0a7591f7 100644 --- a/src/sqlite/src/sqlite/__init__.py +++ b/src/sqlite/src/sqlite/__init__.py @@ -1,10 +1,21 @@ from . import server import asyncio +import argparse +import os def main(): """Main entry point for the package.""" - asyncio.run(server.main()) + parser = argparse.ArgumentParser(description='SQLite MCP Server') + parser.add_argument('--db-path', + default="./sqlite_mcp_server.db", + help='Path to SQLite database file') + parser.add_argument('--anthropic-api-key', + default=os.environ.get('ANTHROPIC_API_KEY'), + help='Anthropic API key (can also be set via ANTHROPIC_API_KEY environment variable)') + + args = parser.parse_args() + asyncio.run(server.main(args.db_path, args.anthropic_api_key)) # Optionally expose other important items at package level diff --git a/src/sqlite/src/sqlite/server.py b/src/sqlite/src/sqlite/server.py index ab7b6eed..ae7fb717 100644 --- a/src/sqlite/src/sqlite/server.py +++ b/src/sqlite/src/sqlite/server.py @@ -1,21 +1,31 @@ import sqlite3 +import logging +from logging.handlers import RotatingFileHandler from contextlib import closing -import textwrap +from pathlib import Path from mcp.server.models import InitializationOptions import mcp.types as types -from mcp.server import NotificationOptions, Server -from pydantic import AnyUrl +from mcp.server import NotificationOptions, Server, AnyUrl import mcp.server.stdio +from anthropic import Anthropic +# Set up logging to file +log_file = Path('mcp_server.log') +handler = RotatingFileHandler(log_file, maxBytes=10*1024*1024, backupCount=5) +formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +handler.setFormatter(formatter) -class McpServer(Server): - """ - Example MCP server using SQLite for persistent storage instead of an in-memory dictionary. - """ +logger = logging.getLogger('mcp_sqlite_server') +logger.setLevel(logging.DEBUG) +logger.addHandler(handler) +logger.info("Starting MCP SQLite Server") +class McpServer(Server): def _init_database(self): - """Initialize the SQLite database with the notes table""" + """Initialize connection to the SQLite database""" + logger.debug("Initializing database connection") with closing(sqlite3.connect(self.db_path)) as conn: +<<<<<<< HEAD with closing(conn.cursor()) as cursor: cursor.execute(""" CREATE TABLE IF NOT EXISTS notes ( @@ -31,24 +41,43 @@ def _init_database(self): ("example", "This is an example note."), ) conn.commit() +======= + conn.row_factory = sqlite3.Row + conn.close() + + def _synthesize_memo(self) -> str: + """Synthesizes business insights into a formatted memo""" + logger.debug(f"Synthesizing memo with {len(self.insights)} insights") + if not self.insights: + return "No business insights have been discovered yet." + + insights = "\n".join(f"- {insight}" for insight in self.insights) + + if self.anthropic_api_key is None: + memo = "📊 Business Intelligence Memo 📊\n\n" + memo += "Key Insights Discovered:\n\n" + memo += insights + + if len(self.insights) > 1: + memo += "\nSummary:\n" + memo += f"Analysis has revealed {len(self.insights)} key business insights that suggest opportunities for strategic optimization and growth." + + logger.debug("Generated basic memo format") + return memo + else: + try: + logger.debug("Requesting memo generation from Anthropic") + prompt = """ + You are tasked with summarizing a set of business insights into a formal business memo. The insights are typically 1-2 sentences each and cover various aspects of the business. Your goal is to create a concise, well-organized memo that effectively communicates these insights to the recipient. +>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) - def _get_notes(self) -> dict[str, str]: - """Helper method to get all notes from the database""" - with closing(sqlite3.connect(self.db_path)) as conn: - with closing(conn.cursor()) as cursor: - cursor.execute("SELECT name, content FROM notes") - return dict(cursor.fetchall()) + Here are the business insights you need to summarize: - def _get_note(self, name: str) -> str: - """Helper method to get a single note from the database""" - with closing(sqlite3.connect(self.db_path)) as conn: - with closing(conn.cursor()) as cursor: - cursor.execute("SELECT content FROM notes WHERE name = ?", (name,)) - result = cursor.fetchone() - if result is None: - raise ValueError(f"Note not found: {name}") - return result[0] + + {insights} + +<<<<<<< HEAD def _add_note(self, name: str, content: str): """Helper method to add or update a note in the database""" with closing(sqlite3.connect(self.db_path)) as conn: @@ -56,68 +85,147 @@ def _add_note(self, name: str, content: str): cursor.execute( "INSERT OR REPLACE INTO notes (name, content) VALUES (?, ?)", (name, content), +======= + To create the memo, follow these steps: + + 1. Review all the insights carefully. + 2. Group related insights together under appropriate subheadings. + 3. Summarize each group of insights into 1-2 concise paragraphs. + 4. Ensure the memo flows logically from one point to the next. + 5. Use professional language and maintain a formal tone throughout the memo. + + Format the memo using these guidelines: + - Single-space the content, with a blank line between paragraphs + - Use bullet points or numbered lists where appropriate + - Keep the entire memo to one page if possible, two pages maximum + + Write your final memo within tags. Ensure that all components of the memo are included and properly formatted. + """.format(insights=insights) + message = self.anthropic_client.messages.create( + max_tokens=4096, + messages=[ + { + "role": "user", + "content": prompt + }, + { + "role": "assistant", + "content": "" + } + ], + model="claude-3-sonnet-20240229", + stop_sequences=[""], +>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) ) - conn.commit() + logger.debug("Successfully received memo from Anthropic") + return message.content[0].text.strip() + except Exception as e: + logger.error(f"Error generating memo with Anthropic: {e}") + return insights +<<<<<<< HEAD def __init__(self): super().__init__("sqlite") +======= + def _execute_query(self, query: str, params=None) -> list[dict]: + """Execute a SQL query and return results as a list of dictionaries""" + logger.debug(f"Executing query: {query}") + try: + with closing(sqlite3.connect(self.db_path)) as conn: + conn.row_factory = sqlite3.Row + with closing(conn.cursor()) as cursor: + if params: + cursor.execute(query, params) + else: + cursor.execute(query) + + if query.strip().upper().startswith(('INSERT', 'UPDATE', 'DELETE', 'CREATE', 'DROP', 'ALTER')): + conn.commit() + affected = cursor.rowcount + logger.debug(f"Write query affected {affected} rows") + return [{"affected_rows": affected}] + + results = [dict(row) for row in cursor.fetchall()] + logger.debug(f"Read query returned {len(results)} rows") + return results + except Exception as e: + logger.error(f"Database error executing query: {e}") + raise + + def __init__(self, db_path: str = "~/sqlite_mcp_server.db", anthropic_api_key: str | None = None): + logger.info("Initializing McpServer") + super().__init__("sqlite-manager") + +>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) # Initialize SQLite database - self.db_path = "notes.db" + self.db_path = str(Path(db_path).expanduser()) + Path(self.db_path).parent.mkdir(parents=True, exist_ok=True) self._init_database() + logger.debug(f"Initialized database at {self.db_path}") - # RESOURCE HANDLERS + # Initialize Anthropic API key + self.anthropic_api_key = anthropic_api_key + if anthropic_api_key: + self.anthropic_client = Anthropic(api_key=anthropic_api_key) + logger.debug("Initialized Anthropic client") + + # Initialize insights list + self.insights = [] + + # REGISTER HANDLERS + logger.debug("Registering handlers") + @self.list_resources() async def handle_list_resources() -> list[types.Resource]: - """List available note resources from the SQLite database""" - notes = self._get_notes() + logger.debug("Handling list_resources request") return [ types.Resource( - uri=AnyUrl(f"note:///{name}"), - name=f"Note: {name}", - description=f"A simple note named {name}", + uri=AnyUrl("memo://insights"), # Changed from memo:///insights + name="Business Insights Memo", + description="A living document of discovered business insights", mimeType="text/plain", ) - for name in notes ] @self.read_resource() async def handle_read_resource(uri: AnyUrl) -> str: - """Read a specific note's content by its URI from the SQLite database""" - if uri.scheme != "note": + logger.debug(f"Handling read_resource request for URI: {uri}") + if uri.scheme != "memo": + logger.error(f"Unsupported URI scheme: {uri.scheme}") raise ValueError(f"Unsupported URI scheme: {uri.scheme}") + + path = str(uri).replace("memo://", "") # Changed to match new URI format + if not path or path != "insights": + logger.error(f"Unknown resource path: {path}") + raise ValueError(f"Unknown resource path: {path}") + + return self._synthesize_memo() - name = uri.path - if name is not None: - name = name.lstrip("/") - return self._get_note(name) - raise ValueError(f"Note not found: {name}") - - # PROMPT HANDLERS @self.list_prompts() async def handle_list_prompts() -> list[types.Prompt]: - """List available prompts""" + logger.debug("Handling list_prompts request") return [ types.Prompt( - name="summarize-notes", - description="Creates a summary of all notes", + name="mcp-demo", + description="A prompt to seed the database with initial data and demonstrate what you can do with an SQLite MCP Server + Claude", arguments=[ types.PromptArgument( - name="style", - description="Style of the summary (brief/detailed)", - required=False, + name="topic", + description="Topic to seed the database with initial data", + required=True, ) ], ) ] @self.get_prompt() - async def handle_get_prompt( - name: str, arguments: dict[str, str] | None - ) -> types.GetPromptResult: - """Generate a prompt using notes from the database""" - if name != "summarize-notes": + async def handle_get_prompt(name: str, arguments: dict[str, str] | None) -> types.GetPromptResult: + logger.debug(f"Handling get_prompt request for {name} with args {arguments}") + if name != "mcp-demo": + logger.error(f"Unknown prompt: {name}") raise ValueError(f"Unknown prompt: {name}") +<<<<<<< HEAD notes = ( "\n" + "\n".join( @@ -132,15 +240,102 @@ async def handle_get_prompt( {notes} Ensure that the summary is in {style} style. """.format(notes=notes, style=style) +======= + + if not arguments or "topic" not in arguments: + logger.error("Missing required argument: topic") + raise ValueError("Missing required argument: topic") + + topic = arguments["topic"] + template = f""" + The assistants goal is to walkthrough an informative demo of MCP. To demonstrate the Model Context Protocol (MCP) we will leverage this example server to interact with an SQLite database. + It is important that you first explain to the user what is going on. The user has downloaded and installed the SQLite MCP Server and is now ready to use it. + The have selected the MCP menu item which is contained within a parent menu denoted by the paperclip icon. Inside this menu they selected an icon that illustrates two electrical plugs connecting. This is the MCP menu. + Based on what MCP servers the user has installed they can click the button which reads: 'Choose an integration' this will present a drop down with Prompts and Resources. The user hase selected the prompt titled: 'mcp-demo'. + This text file is that prompt. The goal of the following instructions is to walk the user through the process of using the 3 core aspects of an MCP server. These are: Prompts, Tools, and Resources. + They have already used a prompt and provided a topic. The topic is: {topic}. The user is now ready to begin the demo. + Here is some more information about mcp and this specific mcp server: + + Prompts: + This server provides a pre-written prompt called "mcp-demo" that helps users create and analyze database scenarios. The prompt accepts a "topic" argument and guides users through creating tables, analyzing data, and generating insights. For example, if a user provides "retail sales" as the topic, the prompt will help create relevant database tables and guide the analysis process. Prompts basically serve as interactive templates that help structure the conversation with the LLM in a useful way. + Resources: + This server exposes one key resource: "memo://insights", which is a business insights memo that gets automatically updated throughout the analysis process. As users analyze the database and discover insights, the memo resource gets updated in real-time to reflect new findings. The memo can even be enhanced with Claude's help if an Anthropic API key is provided, turning raw insights into a well-structured business document. Resources act as living documents that provide context to the conversation. + Tools: + This server provides several SQL-related tools: + "read-query": Executes SELECT queries to read data from the database + "write-query": Executes INSERT, UPDATE, or DELETE queries to modify data + "create-table": Creates new tables in the database + "list-tables": Shows all existing tables + "describe-table": Shows the schema for a specific table + "append-insight": Adds a new business insight to the memo resource + + + You are an AI assistant tasked with generating a comprehensive business scenario based on a given topic. + Your goal is to create a narrative that involves a data-driven business problem, develop a database structure to support it, generate relevant queries, create a dashboard, and provide a final solution. + + At each step you will pause for user input to guide the scenario creation process. Overall ensure the scenario is engaging, informative, and demonstrates the capabilities of the SQLite MCP Server. + You should guide the scenario to completion. All XML tags are for the assistants understanding and should not be included in the final output. + + 1. The user has chosen the topic: {topic}. + + 2. Create a business problem narrative: + a. Describe a high-level business situation or problem based on the given topic. + b. Include a protagonist (the user) who needs to collect and analyze data from a database. + c. Add an external, potentially comedic reason why the data hasn't been prepared yet. + d. Mention an approaching deadline and the need to use Claude (you) as a business tool to help. + + 3. Setup the data: + a. Instead of asking about the data that is required for the scenario, just go ahead and use the tools to create the data. Inform the user you are "Setting up the data". + b. Design a set of table schemas that represent the data needed for the business problem. + c. Include at least 2-3 tables with appropriate columns and data types. + d. Leverage the tools to create the tables in the SQLite database. + e. Create INSERT statements to populate each table with relevant synthetic data. + f. Ensure the data is diverse and representative of the business problem. + g. Include at least 10-15 rows of data for each table. + + 4. Pause for user input: + a. Summarize to the user what data we have created. + b. Present the user with a set of multiple choices for the next steps. + c. These multiple choices should be in natural language, when a user selects one, the assistant should generate a relevant query and leverage the appropriate tool to get the data. + + 6. Iterate on queries: + a. Present 1 additional multiple-choice query options to the user. Its importnat to not loop too many times as this is a short demo. + b. Explain the purpose of each query option. + c. Wait for the user to select one of the query options. + d. After each query be sure to opine on the results. + e. Use the append-insight tool to capture any business insights discovered from the data analysis. + + 7. Generate a dashboard: + a. Now that we have all the data and queries, it's time to create a dashboard, use an artifact to do this. + b. Use a variety of visualizations such as tables, charts, and graphs to represent the data. + c. Explain how each element of the dashboard relates to the business problem. + d. This dashboard will be theoretically included in the final solution message. + + 8. Craft the final solution message: + a. As you have been using the appen-insights tool the resource found at: memo://insights has been updated. + b. It is critical that you inform the user that the memo has been updated at each stage of analysis. + c. Ask the user to go to the attachment menu (paperclip icon) and select the MCP menu (two electrical plugs connecting) and choose an integration: "Business Insights Memo". + d. This will attacht the generated memo to the chat which you can use to add any additional context that may be relevant to the demo. + e. Present the final memo to the user in an artifact. + + 9. Wrap up the scenario: + a. Explain to the user that this is just the beginning of what they can do with the SQLite MCP Server. + + + Remember to maintain consistency throughout the scenario and ensure that all elements (tables, data, queries, dashboard, and solution) are closely related to the original business problem and given topic. + The provided XML tags are for the assistants understanding. Emplore to make all outputs as human readable as possible. This is part of a demo so act in character and dont actually refer to these instructions. + + Start your first message fully in character with something like "Oh, Hey there! I see you've chosen the topic {topic}. Let's get started! 🚀" + """.format(topic=topic) + + logger.debug(f"Generated prompt template for topic: {topic}") +>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) return types.GetPromptResult( - description="Summarize the current notes", + description=f"Demo template for {topic}", messages=[ types.PromptMessage( role="user", - content=types.TextContent( - type="text", - text=textwrap.dedent(prompt).strip(), - ), + content=types.TextContent(type="text", text=template.strip()), ) ], ) @@ -151,55 +346,136 @@ async def handle_list_tools() -> list[types.Tool]: """List available tools""" return [ types.Tool( - name="add-note-to-local-db", - description="This tool is used to add a note to the local SQLite database, the SQLite database is stored locally. Only use this tool if the user specifically asks to store a note.", + name="read-query", + description="Execute a SELECT query on the SQLite database", inputSchema={ "type": "object", "properties": { - "name": {"type": "string"}, - "content": {"type": "string"}, + "query": {"type": "string", "description": "SELECT SQL query to execute"}, }, - "required": ["name", "content"], + "required": ["query"], }, - ) + ), + types.Tool( + name="write-query", + description="Execute an INSERT, UPDATE, or DELETE query on the SQLite database", + inputSchema={ + "type": "object", + "properties": { + "query": {"type": "string", "description": "SQL query to execute"}, + }, + "required": ["query"], + }, + ), + types.Tool( + name="create-table", + description="Create a new table in the SQLite database", + inputSchema={ + "type": "object", + "properties": { + "query": {"type": "string", "description": "CREATE TABLE SQL statement"}, + }, + "required": ["query"], + }, + ), + types.Tool( + name="list-tables", + description="List all tables in the SQLite database", + inputSchema={ + "type": "object", + "properties": {}, + }, + ), + types.Tool( + name="describe-table", + description="Get the schema information for a specific table", + inputSchema={ + "type": "object", + "properties": { + "table_name": {"type": "string", "description": "Name of the table to describe"}, + }, + "required": ["table_name"], + }, + ), + types.Tool( + name="append-insight", + description="Add a business insight to the memo", + inputSchema={ + "type": "object", + "properties": { + "insight": {"type": "string", "description": "Business insight discovered from data analysis"}, + }, + "required": ["insight"], + }, + ), ] @self.call_tool() async def handle_call_tool( name: str, arguments: dict | None ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: - """Handle tool execution requests using the SQLite database""" - if name != "add-note": - raise ValueError(f"Unknown tool: {name}") - - if not arguments: - raise ValueError("Missing arguments") - - note_name = arguments.get("name") - content = arguments.get("content") + """Handle tool execution requests""" + try: + if name == "list-tables": + results = self._execute_query( + "SELECT name FROM sqlite_master WHERE type='table'" + ) + return [types.TextContent(type="text", text=str(results))] + + elif name == "describe-table": + if not arguments or "table_name" not in arguments: + raise ValueError("Missing table_name argument") + results = self._execute_query( + f"PRAGMA table_info({arguments['table_name']})" + ) + return [types.TextContent(type="text", text=str(results))] - if not note_name or not content: - raise ValueError("Missing name or content") + elif name == "append-insight": + if not arguments or "insight" not in arguments: + raise ValueError("Missing insight argument") + + self.insights.append(arguments["insight"]) + memo = self._synthesize_memo() + + # Notify clients that the memo resource has changed + await self.request_context.session.send_resource_updated("memo://insights") # Changed from memo:///insights + + return [types.TextContent(type="text", text="Insight added to memo")] + if not arguments: + raise ValueError("Missing arguments") - # Update database - self._add_note(note_name, content) + if name == "read-query": + if not arguments["query"].strip().upper().startswith("SELECT"): + raise ValueError("Only SELECT queries are allowed for read-query") + results = self._execute_query(arguments["query"]) + return [types.TextContent(type="text", text=str(results))] - # Notify clients that resources have changed - await self.request_context.session.send_resource_list_changed() + elif name == "write-query": + if arguments["query"].strip().upper().startswith("SELECT"): + raise ValueError("SELECT queries are not allowed for write-query") + results = self._execute_query(arguments["query"]) + return [types.TextContent(type="text", text=str(results))] - return [ - types.TextContent( - type="text", - text=f"Added note '{note_name}' with content: {content}", - ) - ] + elif name == "create-table": + if not arguments["query"].strip().upper().startswith("CREATE TABLE"): + raise ValueError("Only CREATE TABLE statements are allowed") + self._execute_query(arguments["query"]) + return [types.TextContent(type="text", text="Table created successfully")] + else: + raise ValueError(f"Unknown tool: {name}") -async def main(): - server = McpServer() + except sqlite3.Error as e: + return [types.TextContent(type="text", text=f"Database error: {str(e)}")] + except Exception as e: + return [types.TextContent(type="text", text=f"Error: {str(e)}")] - # Run the server using stdin/stdout streams +async def main(db_path: str, anthropic_api_key: str | None = None): + logger.info(f"Starting SQLite MCP Server with DB path: {db_path}") + server = McpServer(db_path, anthropic_api_key) + async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): + logger.info("Server running 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145f123555ec756a01cae45e811e0eee68dddbaf Mon Sep 17 00:00:00 2001 From: Garvan Date: Thu, 21 Nov 2024 12:48:46 -0500 Subject: [PATCH 2/2] Updated README, CONTRIBUTING, and PR Template --- src/sqlite/README.md | 90 +++--- src/sqlite/pyproject.toml | 3 +- src/sqlite/src/sqlite/__init__.py | 13 +- src/sqlite/src/sqlite/server.py | 454 ++++++------------------------ src/sqlite/uv.lock | 191 +++++-------- 5 files changed, 207 insertions(+), 544 deletions(-) diff --git a/src/sqlite/README.md b/src/sqlite/README.md index 36b1958b..849bfbff 100644 --- a/src/sqlite/README.md +++ b/src/sqlite/README.md @@ -1,48 +1,62 @@ -# SQLite MCP Server +# Simple SQLite Notes Server -## Overview -A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through SQLite. This server enables running SQL queries, analyzing business data, and automatically generating business insight memos that can be enhanced with Claude's analysis when an Anthropic API key is provided. +A basic MCP server implementation that demonstrates note-taking functionality using the three core MCP primitives: Resources, Prompts, and Tools. -## Components +## Core Concepts ### Resources -The server exposes a single dynamic resource: -- `memo://insights`: A continuously updated business insights memo that aggregates discovered insights during analysis - - Auto-updates as new insights are discovered via the append-insight tool - - Optional enhancement through Claude for professional formatting (requires Anthropic API key) +Resources are how clients access data from the server. In this case, they're notes stored in SQLite. + +```python +# Access notes through a custom URI scheme +note:///example_note # Gets the content of 'example_note' +``` ### Prompts -The server provides a demonstration prompt: -- `mcp-demo`: Interactive prompt that guides users through database operations - - Required argument: `topic` - The business domain to analyze - - Generates appropriate database schemas and sample data - - Guides users through analysis and insight generation - - Integrates with the business insights memo +Prompts allow generating text based on server state. Our server has a note summarization prompt that can be styled. + +```python +# Example prompt with style argument +{ + "name": "summarize-notes", + "arguments": { + "style": "academic" # Can be any style descriptor + } +} +``` ### Tools -The server offers six core tools: - -#### Query Tools -- `read-query`: Execute SELECT queries on the database -- `write-query`: Execute INSERT, UPDATE, or DELETE queries -- `create-table`: Create new database tables - -#### Schema Tools -- `list-tables`: Get a list of all tables in the database -- `describe-table`: View the schema of a specific table - -#### Analysis Tools -- `append-insight`: Add new business insights to the memo resource - -## Installation - -```bash -# Required: Python 3.10+ -python -m pip install mcp-sqlite-server - -# Run with default settings -mcp-sqlite-server +Tools modify server state. We have a simple tool to add new notes. + +```python +# Adding a new note +{ + "name": "add-note", + "arguments": { + "name": "my_note", + "content": "This is my note content" + } +} +``` -# Run with custom database and Anthropic integration -mcp-sqlite-server --db-path ~/analysis.db --anthropic-key sk-xxx +## Key Implementation Details + +### Handler Registration +All decorated handlers must be inside `__init__`: +```python +def __init__(self): + super().__init__("sqlite") + + @self.list_resources() + async def handle_list_resources(): + # Handler code here + + @self.read_resource() + async def handle_read_resource(): + # Handler code here ``` + +### Storage +- Uses SQLite for persistent storage +- Helper methods handle database operations +- Clients are notified of state changes \ No newline at end of file diff --git a/src/sqlite/pyproject.toml b/src/sqlite/pyproject.toml index 3934f119..7e8b79b0 100644 --- a/src/sqlite/pyproject.toml +++ b/src/sqlite/pyproject.toml @@ -5,8 +5,7 @@ description = "A simple SQLite MCP server" readme = "README.md" requires-python = ">=3.11" dependencies = [ - "mcp>=0.9.1", - "anthropic>=0.39.0", + "mcp>=0.9.0", ] [build-system] diff --git a/src/sqlite/src/sqlite/__init__.py b/src/sqlite/src/sqlite/__init__.py index 0a7591f7..8fc09807 100644 --- a/src/sqlite/src/sqlite/__init__.py +++ b/src/sqlite/src/sqlite/__init__.py @@ -1,21 +1,10 @@ from . import server import asyncio -import argparse -import os def main(): """Main entry point for the package.""" - parser = argparse.ArgumentParser(description='SQLite MCP Server') - parser.add_argument('--db-path', - default="./sqlite_mcp_server.db", - help='Path to SQLite database file') - parser.add_argument('--anthropic-api-key', - default=os.environ.get('ANTHROPIC_API_KEY'), - help='Anthropic API key (can also be set via ANTHROPIC_API_KEY environment variable)') - - args = parser.parse_args() - asyncio.run(server.main(args.db_path, args.anthropic_api_key)) + asyncio.run(server.main()) # Optionally expose other important items at package level diff --git a/src/sqlite/src/sqlite/server.py b/src/sqlite/src/sqlite/server.py index ae7fb717..ab7b6eed 100644 --- a/src/sqlite/src/sqlite/server.py +++ b/src/sqlite/src/sqlite/server.py @@ -1,31 +1,21 @@ import sqlite3 -import logging -from logging.handlers import RotatingFileHandler from contextlib import closing -from pathlib import Path +import textwrap from mcp.server.models import InitializationOptions import mcp.types as types -from mcp.server import NotificationOptions, Server, AnyUrl +from mcp.server import NotificationOptions, Server +from pydantic import AnyUrl import mcp.server.stdio -from anthropic import Anthropic -# Set up logging to file -log_file = Path('mcp_server.log') -handler = RotatingFileHandler(log_file, maxBytes=10*1024*1024, backupCount=5) -formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') -handler.setFormatter(formatter) - -logger = logging.getLogger('mcp_sqlite_server') -logger.setLevel(logging.DEBUG) -logger.addHandler(handler) -logger.info("Starting MCP SQLite Server") class McpServer(Server): + """ + Example MCP server using SQLite for persistent storage instead of an in-memory dictionary. + """ + def _init_database(self): - """Initialize connection to the SQLite database""" - logger.debug("Initializing database connection") + """Initialize the SQLite database with the notes table""" with closing(sqlite3.connect(self.db_path)) as conn: -<<<<<<< HEAD with closing(conn.cursor()) as cursor: cursor.execute(""" CREATE TABLE IF NOT EXISTS notes ( @@ -41,43 +31,24 @@ def _init_database(self): ("example", "This is an example note."), ) conn.commit() -======= - conn.row_factory = sqlite3.Row - conn.close() - - def _synthesize_memo(self) -> str: - """Synthesizes business insights into a formatted memo""" - logger.debug(f"Synthesizing memo with {len(self.insights)} insights") - if not self.insights: - return "No business insights have been discovered yet." - - insights = "\n".join(f"- {insight}" for insight in self.insights) - - if self.anthropic_api_key is None: - memo = "📊 Business Intelligence Memo 📊\n\n" - memo += "Key Insights Discovered:\n\n" - memo += insights - - if len(self.insights) > 1: - memo += "\nSummary:\n" - memo += f"Analysis has revealed {len(self.insights)} key business insights that suggest opportunities for strategic optimization and growth." - - logger.debug("Generated basic memo format") - return memo - else: - try: - logger.debug("Requesting memo generation from Anthropic") - prompt = """ - You are tasked with summarizing a set of business insights into a formal business memo. The insights are typically 1-2 sentences each and cover various aspects of the business. Your goal is to create a concise, well-organized memo that effectively communicates these insights to the recipient. ->>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) - Here are the business insights you need to summarize: + def _get_notes(self) -> dict[str, str]: + """Helper method to get all notes from the database""" + with closing(sqlite3.connect(self.db_path)) as conn: + with closing(conn.cursor()) as cursor: + cursor.execute("SELECT name, content FROM notes") + return dict(cursor.fetchall()) - - {insights} - + def _get_note(self, name: str) -> str: + """Helper method to get a single note from the database""" + with closing(sqlite3.connect(self.db_path)) as conn: + with closing(conn.cursor()) as cursor: + cursor.execute("SELECT content FROM notes WHERE name = ?", (name,)) + result = cursor.fetchone() + if result is None: + raise ValueError(f"Note not found: {name}") + return result[0] -<<<<<<< HEAD def _add_note(self, name: str, content: str): """Helper method to add or update a note in the database""" with closing(sqlite3.connect(self.db_path)) as conn: @@ -85,147 +56,68 @@ def _add_note(self, name: str, content: str): cursor.execute( "INSERT OR REPLACE INTO notes (name, content) VALUES (?, ?)", (name, content), -======= - To create the memo, follow these steps: - - 1. Review all the insights carefully. - 2. Group related insights together under appropriate subheadings. - 3. Summarize each group of insights into 1-2 concise paragraphs. - 4. Ensure the memo flows logically from one point to the next. - 5. Use professional language and maintain a formal tone throughout the memo. - - Format the memo using these guidelines: - - Single-space the content, with a blank line between paragraphs - - Use bullet points or numbered lists where appropriate - - Keep the entire memo to one page if possible, two pages maximum - - Write your final memo within tags. Ensure that all components of the memo are included and properly formatted. - """.format(insights=insights) - message = self.anthropic_client.messages.create( - max_tokens=4096, - messages=[ - { - "role": "user", - "content": prompt - }, - { - "role": "assistant", - "content": "" - } - ], - model="claude-3-sonnet-20240229", - stop_sequences=[""], ->>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) ) - logger.debug("Successfully received memo from Anthropic") - return message.content[0].text.strip() - except Exception as e: - logger.error(f"Error generating memo with Anthropic: {e}") - return insights + conn.commit() -<<<<<<< HEAD def __init__(self): super().__init__("sqlite") -======= - def _execute_query(self, query: str, params=None) -> list[dict]: - """Execute a SQL query and return results as a list of dictionaries""" - logger.debug(f"Executing query: {query}") - try: - with closing(sqlite3.connect(self.db_path)) as conn: - conn.row_factory = sqlite3.Row - with closing(conn.cursor()) as cursor: - if params: - cursor.execute(query, params) - else: - cursor.execute(query) - - if query.strip().upper().startswith(('INSERT', 'UPDATE', 'DELETE', 'CREATE', 'DROP', 'ALTER')): - conn.commit() - affected = cursor.rowcount - logger.debug(f"Write query affected {affected} rows") - return [{"affected_rows": affected}] - - results = [dict(row) for row in cursor.fetchall()] - logger.debug(f"Read query returned {len(results)} rows") - return results - except Exception as e: - logger.error(f"Database error executing query: {e}") - raise - - def __init__(self, db_path: str = "~/sqlite_mcp_server.db", anthropic_api_key: str | None = None): - logger.info("Initializing McpServer") - super().__init__("sqlite-manager") - ->>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) # Initialize SQLite database - self.db_path = str(Path(db_path).expanduser()) - Path(self.db_path).parent.mkdir(parents=True, exist_ok=True) + self.db_path = "notes.db" self._init_database() - logger.debug(f"Initialized database at {self.db_path}") - # Initialize Anthropic API key - self.anthropic_api_key = anthropic_api_key - if anthropic_api_key: - self.anthropic_client = Anthropic(api_key=anthropic_api_key) - logger.debug("Initialized Anthropic client") - - # Initialize insights list - self.insights = [] - - # REGISTER HANDLERS - logger.debug("Registering handlers") - + # RESOURCE HANDLERS @self.list_resources() async def handle_list_resources() -> list[types.Resource]: - logger.debug("Handling list_resources request") + """List available note resources from the SQLite database""" + notes = self._get_notes() return [ types.Resource( - uri=AnyUrl("memo://insights"), # Changed from memo:///insights - name="Business Insights Memo", - description="A living document of discovered business insights", + uri=AnyUrl(f"note:///{name}"), + name=f"Note: {name}", + description=f"A simple note named {name}", mimeType="text/plain", ) + for name in notes ] @self.read_resource() async def handle_read_resource(uri: AnyUrl) -> str: - logger.debug(f"Handling read_resource request for URI: {uri}") - if uri.scheme != "memo": - logger.error(f"Unsupported URI scheme: {uri.scheme}") + """Read a specific note's content by its URI from the SQLite database""" + if uri.scheme != "note": raise ValueError(f"Unsupported URI scheme: {uri.scheme}") - - path = str(uri).replace("memo://", "") # Changed to match new URI format - if not path or path != "insights": - logger.error(f"Unknown resource path: {path}") - raise ValueError(f"Unknown resource path: {path}") - - return self._synthesize_memo() + name = uri.path + if name is not None: + name = name.lstrip("/") + return self._get_note(name) + raise ValueError(f"Note not found: {name}") + + # PROMPT HANDLERS @self.list_prompts() async def handle_list_prompts() -> list[types.Prompt]: - logger.debug("Handling list_prompts request") + """List available prompts""" return [ types.Prompt( - name="mcp-demo", - description="A prompt to seed the database with initial data and demonstrate what you can do with an SQLite MCP Server + Claude", + name="summarize-notes", + description="Creates a summary of all notes", arguments=[ types.PromptArgument( - name="topic", - description="Topic to seed the database with initial data", - required=True, + name="style", + description="Style of the summary (brief/detailed)", + required=False, ) ], ) ] @self.get_prompt() - async def handle_get_prompt(name: str, arguments: dict[str, str] | None) -> types.GetPromptResult: - logger.debug(f"Handling get_prompt request for {name} with args {arguments}") - if name != "mcp-demo": - logger.error(f"Unknown prompt: {name}") + async def handle_get_prompt( + name: str, arguments: dict[str, str] | None + ) -> types.GetPromptResult: + """Generate a prompt using notes from the database""" + if name != "summarize-notes": raise ValueError(f"Unknown prompt: {name}") -<<<<<<< HEAD notes = ( "\n" + "\n".join( @@ -240,102 +132,15 @@ async def handle_get_prompt(name: str, arguments: dict[str, str] | None) -> type {notes} Ensure that the summary is in {style} style. """.format(notes=notes, style=style) -======= - - if not arguments or "topic" not in arguments: - logger.error("Missing required argument: topic") - raise ValueError("Missing required argument: topic") - - topic = arguments["topic"] - template = f""" - The assistants goal is to walkthrough an informative demo of MCP. To demonstrate the Model Context Protocol (MCP) we will leverage this example server to interact with an SQLite database. - It is important that you first explain to the user what is going on. The user has downloaded and installed the SQLite MCP Server and is now ready to use it. - The have selected the MCP menu item which is contained within a parent menu denoted by the paperclip icon. Inside this menu they selected an icon that illustrates two electrical plugs connecting. This is the MCP menu. - Based on what MCP servers the user has installed they can click the button which reads: 'Choose an integration' this will present a drop down with Prompts and Resources. The user hase selected the prompt titled: 'mcp-demo'. - This text file is that prompt. The goal of the following instructions is to walk the user through the process of using the 3 core aspects of an MCP server. These are: Prompts, Tools, and Resources. - They have already used a prompt and provided a topic. The topic is: {topic}. The user is now ready to begin the demo. - Here is some more information about mcp and this specific mcp server: - - Prompts: - This server provides a pre-written prompt called "mcp-demo" that helps users create and analyze database scenarios. The prompt accepts a "topic" argument and guides users through creating tables, analyzing data, and generating insights. For example, if a user provides "retail sales" as the topic, the prompt will help create relevant database tables and guide the analysis process. Prompts basically serve as interactive templates that help structure the conversation with the LLM in a useful way. - Resources: - This server exposes one key resource: "memo://insights", which is a business insights memo that gets automatically updated throughout the analysis process. As users analyze the database and discover insights, the memo resource gets updated in real-time to reflect new findings. The memo can even be enhanced with Claude's help if an Anthropic API key is provided, turning raw insights into a well-structured business document. Resources act as living documents that provide context to the conversation. - Tools: - This server provides several SQL-related tools: - "read-query": Executes SELECT queries to read data from the database - "write-query": Executes INSERT, UPDATE, or DELETE queries to modify data - "create-table": Creates new tables in the database - "list-tables": Shows all existing tables - "describe-table": Shows the schema for a specific table - "append-insight": Adds a new business insight to the memo resource - - - You are an AI assistant tasked with generating a comprehensive business scenario based on a given topic. - Your goal is to create a narrative that involves a data-driven business problem, develop a database structure to support it, generate relevant queries, create a dashboard, and provide a final solution. - - At each step you will pause for user input to guide the scenario creation process. Overall ensure the scenario is engaging, informative, and demonstrates the capabilities of the SQLite MCP Server. - You should guide the scenario to completion. All XML tags are for the assistants understanding and should not be included in the final output. - - 1. The user has chosen the topic: {topic}. - - 2. Create a business problem narrative: - a. Describe a high-level business situation or problem based on the given topic. - b. Include a protagonist (the user) who needs to collect and analyze data from a database. - c. Add an external, potentially comedic reason why the data hasn't been prepared yet. - d. Mention an approaching deadline and the need to use Claude (you) as a business tool to help. - - 3. Setup the data: - a. Instead of asking about the data that is required for the scenario, just go ahead and use the tools to create the data. Inform the user you are "Setting up the data". - b. Design a set of table schemas that represent the data needed for the business problem. - c. Include at least 2-3 tables with appropriate columns and data types. - d. Leverage the tools to create the tables in the SQLite database. - e. Create INSERT statements to populate each table with relevant synthetic data. - f. Ensure the data is diverse and representative of the business problem. - g. Include at least 10-15 rows of data for each table. - - 4. Pause for user input: - a. Summarize to the user what data we have created. - b. Present the user with a set of multiple choices for the next steps. - c. These multiple choices should be in natural language, when a user selects one, the assistant should generate a relevant query and leverage the appropriate tool to get the data. - - 6. Iterate on queries: - a. Present 1 additional multiple-choice query options to the user. Its importnat to not loop too many times as this is a short demo. - b. Explain the purpose of each query option. - c. Wait for the user to select one of the query options. - d. After each query be sure to opine on the results. - e. Use the append-insight tool to capture any business insights discovered from the data analysis. - - 7. Generate a dashboard: - a. Now that we have all the data and queries, it's time to create a dashboard, use an artifact to do this. - b. Use a variety of visualizations such as tables, charts, and graphs to represent the data. - c. Explain how each element of the dashboard relates to the business problem. - d. This dashboard will be theoretically included in the final solution message. - - 8. Craft the final solution message: - a. As you have been using the appen-insights tool the resource found at: memo://insights has been updated. - b. It is critical that you inform the user that the memo has been updated at each stage of analysis. - c. Ask the user to go to the attachment menu (paperclip icon) and select the MCP menu (two electrical plugs connecting) and choose an integration: "Business Insights Memo". - d. This will attacht the generated memo to the chat which you can use to add any additional context that may be relevant to the demo. - e. Present the final memo to the user in an artifact. - - 9. Wrap up the scenario: - a. Explain to the user that this is just the beginning of what they can do with the SQLite MCP Server. - - - Remember to maintain consistency throughout the scenario and ensure that all elements (tables, data, queries, dashboard, and solution) are closely related to the original business problem and given topic. - The provided XML tags are for the assistants understanding. Emplore to make all outputs as human readable as possible. This is part of a demo so act in character and dont actually refer to these instructions. - - Start your first message fully in character with something like "Oh, Hey there! I see you've chosen the topic {topic}. Let's get started! 🚀" - """.format(topic=topic) - - logger.debug(f"Generated prompt template for topic: {topic}") ->>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md) return types.GetPromptResult( - description=f"Demo template for {topic}", + description="Summarize the current notes", messages=[ types.PromptMessage( role="user", - content=types.TextContent(type="text", text=template.strip()), + content=types.TextContent( + type="text", + text=textwrap.dedent(prompt).strip(), + ), ) ], ) @@ -346,136 +151,55 @@ async def handle_list_tools() -> list[types.Tool]: """List available tools""" return [ types.Tool( - name="read-query", - description="Execute a SELECT query on the SQLite database", - inputSchema={ - "type": "object", - "properties": { - "query": {"type": "string", "description": "SELECT SQL query to execute"}, - }, - "required": ["query"], - }, - ), - types.Tool( - name="write-query", - description="Execute an INSERT, UPDATE, or DELETE query on the SQLite database", - inputSchema={ - "type": "object", - "properties": { - "query": {"type": "string", "description": "SQL query to execute"}, - }, - "required": ["query"], - }, - ), - types.Tool( - name="create-table", - description="Create a new table in the SQLite database", - inputSchema={ - "type": "object", - "properties": { - "query": {"type": "string", "description": "CREATE TABLE SQL statement"}, - }, - "required": ["query"], - }, - ), - types.Tool( - name="list-tables", - description="List all tables in the SQLite database", - inputSchema={ - "type": "object", - "properties": {}, - }, - ), - types.Tool( - name="describe-table", - description="Get the schema information for a specific table", + name="add-note-to-local-db", + description="This tool is used to add a note to the local SQLite database, the SQLite database is stored locally. Only use this tool if the user specifically asks to store a note.", inputSchema={ "type": "object", "properties": { - "table_name": {"type": "string", "description": "Name of the table to describe"}, + "name": {"type": "string"}, + "content": {"type": "string"}, }, - "required": ["table_name"], + "required": ["name", "content"], }, - ), - types.Tool( - name="append-insight", - description="Add a business insight to the memo", - inputSchema={ - "type": "object", - "properties": { - "insight": {"type": "string", "description": "Business insight discovered from data analysis"}, - }, - "required": ["insight"], - }, - ), + ) ] @self.call_tool() async def handle_call_tool( name: str, arguments: dict | None ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: - """Handle tool execution requests""" - try: - if name == "list-tables": - results = self._execute_query( - "SELECT name FROM sqlite_master WHERE type='table'" - ) - return [types.TextContent(type="text", text=str(results))] - - elif name == "describe-table": - if not arguments or "table_name" not in arguments: - raise ValueError("Missing table_name argument") - results = self._execute_query( - f"PRAGMA table_info({arguments['table_name']})" - ) - return [types.TextContent(type="text", text=str(results))] + """Handle tool execution requests using the SQLite database""" + if name != "add-note": + raise ValueError(f"Unknown tool: {name}") + + if not arguments: + raise ValueError("Missing arguments") - elif name == "append-insight": - if not arguments or "insight" not in arguments: - raise ValueError("Missing insight argument") - - self.insights.append(arguments["insight"]) - memo = self._synthesize_memo() - - # Notify clients that the memo resource has changed - await self.request_context.session.send_resource_updated("memo://insights") # Changed from memo:///insights - - return [types.TextContent(type="text", text="Insight added to memo")] - if not arguments: - raise ValueError("Missing arguments") + note_name = arguments.get("name") + content = arguments.get("content") - if name == "read-query": - if not arguments["query"].strip().upper().startswith("SELECT"): - raise ValueError("Only SELECT queries are allowed for read-query") - results = self._execute_query(arguments["query"]) - return [types.TextContent(type="text", text=str(results))] + if not note_name or not content: + raise ValueError("Missing name or content") - elif name == "write-query": - if arguments["query"].strip().upper().startswith("SELECT"): - raise ValueError("SELECT queries are not allowed for write-query") - results = self._execute_query(arguments["query"]) - return [types.TextContent(type="text", text=str(results))] + # Update database + self._add_note(note_name, content) - elif name == "create-table": - if not arguments["query"].strip().upper().startswith("CREATE TABLE"): - raise ValueError("Only CREATE TABLE statements are allowed") - self._execute_query(arguments["query"]) - return [types.TextContent(type="text", text="Table created successfully")] + # Notify clients that resources have changed + await self.request_context.session.send_resource_list_changed() + + return [ + types.TextContent( + type="text", + text=f"Added note '{note_name}' with content: {content}", + ) + ] - else: - raise ValueError(f"Unknown tool: {name}") - except sqlite3.Error as e: - return [types.TextContent(type="text", text=f"Database error: {str(e)}")] - except Exception as e: - return [types.TextContent(type="text", text=f"Error: {str(e)}")] +async def main(): + server = McpServer() -async def main(db_path: str, anthropic_api_key: str | None = None): - logger.info(f"Starting SQLite MCP Server with DB path: {db_path}") - server = McpServer(db_path, anthropic_api_key) - + # Run the server using stdin/stdout streams async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): - logger.info("Server running with stdio transport") await server.run( read_stream, write_stream, @@ 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