GitHub Repository Analyzer

Architectural Design: GitHub Repository Analyzer MCP Server =========================================================== System Components ----------------- The GitHub Repository Analyzer is built with a modular architecture consisting of four main components: ### 1\. MCP Server This is the core component that implements the Model Context Protocol and acts as the interface between LLM clients and the GitHub API. The MCP Server: - Registers and exposes tools and resources - Handles communication with LLM clients - Manages authentication and session state - Processes requests and returns responses in MCP-compatible formats ### 2\. GitHub API Client This component handles all interactions with the GitHub API, including: - Authentication with GitHub - Making API requests - Handling rate limiting - Error management and retries - Parsing GitHub responses ### 3\. Analysis Tools These are specialized functions that perform specific analysis tasks: - Repository information retrieval - Issue listing and analysis - Commit history extraction - Activity metrics calculation - Chart and visualization generation ### 4\. Data Resources These provide access to repository data as resources: - README content - Repository documentation - Other file content as needed Component Interaction Flow -------------------------- ![System Architecture Diagram] 1. **User Request**: A user asks an LLM client (like Claude Desktop) a question about a GitHub repository 2. **Tool Discovery**: The LLM client queries the MCP Server to discover available tools 3. **Tool Selection**: The LLM determines which tools are needed to answer the user's question 4. **Tool Invocation**: The LLM client calls the appropriate tools on the MCP Server 5. **GitHub Interaction**: The MCP Server uses the GitHub API Client to retrieve requested data 6. **Data Processing**: The server processes and analyzes the raw GitHub data 7. **Response Generation**: The processed data is returned to the LLM client 8. **User Response**: The LLM client uses the data to generate an informed response for the user Data Flow Diagram ----------------- ``` User Question → LLM Client → MCP Server → GitHub API Client → GitHub API ↓ User Response ← LLM Client ← Processed Data ← MCP Server ← Raw GitHub Data ``` Tool Architecture ----------------- Each tool in the system follows a standard pattern: 1. **Input Definition**: Clearly defined parameters (e.g., repository name, time period) 2. **Processing Logic**: The analysis or retrieval logic that uses the GitHub API 3. **Output Formatting**: Structured response that's useful for LLM interpretation 4. **Error Handling**: Graceful handling of API limits, authentication issues, and other potential errors Security Considerations ----------------------- The architecture incorporates several security measures: - GitHub API tokens are stored as environment variables, not hardcoded - The server only exposes specific, controlled functionality - API requests are limited to explicitly requested repositories - Error messages are designed to avoid leaking sensitive information Extensibility Design -------------------- The system is designed to be extensible: - New tools can be added by creating additional functions - Existing tools can be enhanced without disrupting the overall architecture - Additional API integrations could be added beyond GitHub Checkpoint List --------------- - [x] Understand the four main components of the system - [x] Visualize the flow of data between components - [x] Comprehend the standard pattern for tool implementation - [x] Recognize the security considerations in the design - [x] Understand how the system is designed for extensibility