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mcp-server-documentation

MCP.Pizza Chef: PicardRaphael

The MCP Documentation Search Server is a FastMCP-based server that enables AI systems to perform intelligent, unified searches across multiple popular framework and library documentations such as Next.js, Tailwind CSS, Framer Motion, LangChain, and more. It features smart name resolution and asynchronous processing to quickly retrieve relevant documentation, streamlining access to diverse technical resources through a single interface.

Use This MCP server To

Search multiple framework documentations with one query Resolve library name variations intelligently during searches Provide AI models with unified access to diverse docs Enable asynchronous processing for faster documentation retrieval Integrate documentation search into developer tools and IDEs Support multi-library documentation lookup for complex projects

README

MCP Documentation Search Server

A powerful documentation search server built with FastMCP, enabling AI systems to intelligently search across multiple popular framework and library documentations. This tool ensures that AI models can quickly access and retrieve relevant information from various documentation sources using a unified interface.

๐ŸŒŸ Features

  • ๐Ÿ“š Multi-Library Support: Search documentation across multiple libraries:

  • ๐Ÿ” Intelligent Search

    • Smart name resolution for library variations
    • DuckDuckGo-powered search for accurate results
    • Site-specific search targeting
  • โšก Performance Features

    • Asynchronous processing
    • Efficient web request handling
    • Parallel content fetching
  • ๐Ÿ›ก๏ธ Robust Error Handling

    • Network timeout management
    • Invalid input validation
    • HTTP error handling
    • Request failure recovery

๐Ÿ“‹ Requirements

  • Python 3.8+
  • pip or uv package manager
  • Virtual environment (recommended)

๐Ÿš€ Quick Start

  1. Clone the Repository
git clone <repository-url>
cd mcp-server
  1. Set Up Virtual Environment
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate
  1. Install Dependencies
pip install -r requirements.txt
  1. Run the Server
python main.py

๐Ÿ’ป Usage

Basic Usage

from main import get_docs

# Search Framer Motion documentation
result = await get_docs(
    query="how to animate on scroll",
    library="framer-motion"
)

# Search Next.js documentation
result = await get_docs(
    query="how to use app router",
    library="next"
)

Library Name Variations

The system intelligently handles various library name formats:

# All these calls will work the same way
await get_docs(query="animations", library="framer")
await get_docs(query="animations", library="framermotion")
await get_docs(query="animations", library="framer-motion")
await get_docs(query="animations", library="motion")

๐Ÿงช Testing

The project includes a comprehensive test suite to ensure reliability and correctness. Tests are organized into three main categories:

Test Structure

  • Unit Tests: Test individual components in isolation

    • test_utils.py: Tests for library name normalization and URL retrieval
    • test_services.py: Tests for web search and content fetching services
  • Integration Tests: Test how components work together

    • test_main.py: Tests for the main API function get_docs

Running Tests

To run all tests:

python -m pytest

To run specific test modules:

python -m pytest tests/test_utils.py
python -m pytest tests/test_services.py
python -m pytest tests/test_main.py

To run tests with verbose output:

python -m pytest -v

Test Coverage

The tests cover:

  • โœ… Library name normalization and validation
  • โœ… URL retrieval for different libraries
  • โœ… Web search functionality
  • โœ… Content fetching and error handling
  • โœ… Documentation search integration
  • โœ… API input validation and error handling
  • โœ… Alias resolution for different library name formats

Asynchronous Testing

The project uses a custom run_async helper function to test asynchronous code in a synchronous test environment. This approach allows for testing async functions without requiring complex test setup.

๐Ÿ—๏ธ Project Structure

mcp-server/
โ”œโ”€โ”€ main.py          # Entry point and FastMCP tool definition
โ”œโ”€โ”€ config.py        # Configuration settings and constants
โ”œโ”€โ”€ services.py      # Web search and content fetching services
โ”œโ”€โ”€ utils.py         # Utility functions for library name handling
โ”œโ”€โ”€ tests/           # Test suite
โ”‚   โ”œโ”€โ”€ test_utils.py    # Tests for utility functions
โ”‚   โ”œโ”€โ”€ test_services.py # Tests for web services
โ”‚   โ”œโ”€โ”€ test_main.py     # Tests for main API
โ”‚   โ””โ”€โ”€ conftest.py      # Pytest configuration
โ”œโ”€โ”€ requirements.txt # Project dependencies
โ””โ”€โ”€ README.md        # Documentation

๐Ÿ”ง Configuration

Supported Libraries

To add a new library:

  1. Add the documentation URL in config.py:
DOCS_URLS = {
    "new-library": "https://docs.new-library.com",
    # ... existing entries
}
  1. Add common aliases:
LIBRARY_ALIASES = {
    "new-lib": "new-library",
    # ... existing entries
}

HTTP Settings

Modify in config.py:

HTTP_TIMEOUT = 30.0        # Timeout in seconds
MAX_SEARCH_RESULTS = 2     # Number of search results to fetch

๐Ÿค Contributing

We welcome contributions! Here's how you can help:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests for new functionality
  5. Ensure all tests pass
  6. Submit a pull request

Adding New Libraries

  1. Update DOCS_URLS in config.py
  2. Add relevant aliases in LIBRARY_ALIASES
  3. Test the integration
  4. Update documentation
  5. Submit a pull request

๐Ÿ› Troubleshooting

Common issues and solutions:

  • TimeoutError: Increase HTTP_TIMEOUT in config.py
  • No Results: Try different search terms or verify the library name
  • HTTP Errors: Check your internet connection and the documentation URL

๐Ÿ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

๐Ÿ™ Acknowledgments

  • FastMCP for the core functionality
  • DuckDuckGo for search capabilities
  • pytest for testing framework
  • All supported documentation providers

mcp-server-documentation FAQ

How does the server handle different library name variations?
It uses intelligent name resolution to match variations and aliases for accurate search results.
Can this server be integrated with IDEs or developer tools?
Yes, it provides a unified API that can be embedded into IDEs and other developer environments.
Does the server support asynchronous search queries?
Yes, it processes queries asynchronously to improve response times and handle multiple requests efficiently.
Which documentations are supported by this server?
It supports popular frameworks and libraries including Next.js, Tailwind CSS, Framer Motion, LangChain, LangGraph, and FastMCP.
Is this server limited to JavaScript/TypeScript documentation?
No, while it includes many JS/TS docs, it can be extended to support other documentation sources.
How does this server improve AI model interactions?
By providing quick, unified access to relevant documentation, it enables AI models like OpenAI, Claude, and Gemini to retrieve accurate technical information efficiently.
What technology powers this documentation search server?
It is built on FastMCP, leveraging its protocol for structured, real-time context and tool integration.