Model Context Protocol (MCP) Server for Real-time Library Documentation

Overview: What is the MCP Server?

The Model Context Protocol (MCP) Server is specifically designed to enhance Large Language Models (LLMs), such as Anthropic's Claude, by providing them with real-time access to the latest documentation of popular Python libraries. This ensures that LLMs generate code suggestions based on current and up-to-date information, making AI development more efficient and error-free.

🔧 Core Features & MCP Capabilities

Implementation Details

• MCP Standard: The server adheres to the Model Context Protocol, which enables seamless communication between AI applications (e.g., Claude Desktop, Continue, Cursor) and external tools or data sources.
• get_docs Tool: This tool exposes a method for searching official documentation sites. It integrates with Serper API to perform precise searches limited to specific domains like Python Langchain, LlamaIndex, OpenAI.
• Content Fetching & Parsing: Using httpx and BeautifulSoup, the server retrieves and parses top search result content from official documentation sites.

Protocol Details

The Model Context Protocol defines a structured method for AI applications to interact with external tools, data sources, or context providers. Here's how it works:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[data source/tool]
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8

• Client-Server Interaction: The MCP client within the AI application initiates a request to the server, passing necessary parameters and queries.
• Tool Invocation: The MCP Server then executes specific tools (like get_docs), which fetches relevant documentation from official sites.
• Context Retrieval & Return: After parsing the fetched content, the server returns it as context back to the client for further processing.

Compatibility

The server is compatible with various AI applications like Claude Desktop and Continue. Below is a compatibility matrix detailing its support level:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

⚙️ MCP Architecture & Protocol Implementation

Technical Details

The server is built using modern Python tools and libraries, including:

• Python 3.11+: Ensures compatibility with newer features and optimizations.
• Asynchronous Operations: Uses asyncio for efficient handling of multiple requests simultaneously.
• FastMCP: A high-performance implementation of the Model Context Protocol in Python.

The architecture consists of several key components:

1. Initialization & Setup

   • The server starts by initializing necessary dependencies and creating a virtual environment to ensure isolated runtime configurations.

2. Tool Invocation

   • The get_docs tool is primarily responsible for performing site-specific Google searches using the Serper API.
   • It constructs queries targeting official documentation sites, ensuring that only relevant content from trusted sources is retrieved.

3. Content Fetching & Parsing

   • Utilizes httpx and BeautifulSoup to fetch and parse HTML content from search results, extracting the most relevant text snippets for use in context.

4. MCP Protocol Integration

   • The server listens for incoming MCP client requests on standard input/output channels (stdio), facilitating seamless communication between AI applications and external tools.

🚀 Getting Started with Installation

To get started with setting up the MCP Server, follow these detailed steps:

1. Clone the Repository (if applicable):

   git clone <your-repository-url>
   cd <your-repository-name>
   

2. Initialize Project (if starting fresh):

   # If you haven't cloned a repo with pyproject.toml
   uv init mcp-server
   cd mcp-server
   

3. Create and Activate Virtual Environment:

   uv venv
   # Activate (Linux/macOS):
   source .venv/bin/activate
   # Activate (Windows PowerShell):
   . \.venv\Scripts\Activate.ps1
   # Activate (Windows Cmd):
   .\.venv\Scripts\activate.bat
   

4. Install Dependencies:

   uv add "mcp[cli]" httpx python-dotenv bs4
   # Or, if dependencies are listed in pyproject.toml:
   # uv sync
   

5. Configure Environment Variables:

   Create a file named .env in the root directory of the project and add your Serper API key:

   SERPER_API_KEY=your_actual_serper_api_key_here
   

   Ensure that this file is included in your .gitignore.

💡 Key Use Cases in AI Workflows

Real-time Integration for Code Generation

• Case Study: A developer needs to integrate a new library into their project but wants to ensure the generated code follows best practices. The MCP Server fetches up-to-date documentation and injects it contextually, helping the LLM craft accurate and efficient code.

  # Example scenario: Fetching Langchain docs for `create_agent` method
  query = "create_agent"
  library = "Langchain"
  response = mcp_client.invoke("get_docs", {"query": query, "library": library})
  print(response)
  

Enhancing Code Recommendations with Real-time Documentation

• Case Study: During a coding session, the developer faces an issue and needs immediate guidance on a specific method. The MCP Server can quickly fetch detailed documentation snippets from Langchain or LlamaIndex, thereby speeding up debugging and resolution steps.

🔌 Integration with MCP Clients

Detailed Setup for Claude Desktop

1. Go to Settings > Developer > Edit Configuration.

2. Add an entry under mcpServers.

   {
     "mcpServers": [
       {
         "name": "docs-helper", // Or any name you prefer
         "command": ["/full/path/to/your/.venv/bin/python", "-m", "uv", "run", "main.py"],
         "workingDirectory": "/full/path/to/your/mcp-server/project"
       }
     ]
   }
   

3. Restart Claude Desktop, and a tool hammer icon should appear for invoking the server.

Integration with Claude Code (CLI)

1. Use the claude mcp add command interactively or with flags.

2. Example interactive session prompts:

   Server Name: documentation-fetcher  # Or any name you prefer
   Project Type: local
   Command: /full/path/to/uv run main.py
   Working Directory: /full/path/to/your/mcp-server/project
   

3. A similar setup as above for claude to integrate with the MCP Server.

Configuration Code Sample

{
  "mcpServers": {
    "documentation-fetcher": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-documentation"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

1. Enhanced AI Application Through MCP

Real-world Use Case: Dynamic Code Generation

By integrating this MCP Server, developers can significantly enhance their productivity by:

• Reducing Errors: Real-time access to documentation ensures that generated code adheres to best practices.
• Faster Debugging: Quick retrieval of relevant documentation snippets saves time and effort.

Real-world Use Case: Contextual Help During Coding

The server provides contextual help during coding sessions, ensuring that developers have the necessary information at their fingertips:

• Immediate Guidance: Instant access to detailed methods and usage examples helps in resolving issues more efficiently.
• Improved Collaboration: Team members can collaborate effectively by leveraging up-to-date documentation.

5. Addressing MCP Integration Challenges

Q&A Section

1. How do I set up the server for my AI application?

   • Follow the installation steps outlined in the guide, ensuring that all dependencies are correctly installed and environment variables are configured.

2. What if the fetched content is not relevant or complete?

   • The server leverages the Serper API to perform precise searches within specific domains. To improve accuracy, adjust search parameters or provide more context.

3. Can this integration be used with any AI application?

   • Yes, as long as the application supports MCP protocol and uses compatible tools for invoking external commands and fetching data.

4. How does this server ensure the security of API keys and other sensitive information?

   • Sensitive information is stored in environment variables which are not committed to version control systems like .gitignore. Use secure methods to handle API keys during deployment.

5. What if I need additional tools or resources beyond those provided by this server?

   • You can extend the functionality of the server by integrating with other APIs and data sources as needed, ensuring that your AI application remains well-informed and up-to-date.

By following these detailed steps and guidelines, developers can effectively leverage the MCP Server to enhance their AI applications, ensuring that they remain fully integrated and responsive. This comprehensive approach not only improves productivity but also promotes better practices in code generation and maintenance.