MCP Codebase Insight

by tosin2013

Integrations

  • Enables configuration of the MCP server through environment variables loaded from .env files, simplifying deployment setup and configuration management.

  • Provides containerized deployment of the MCP Codebase Insight server with support for environment variables and volume mounting for persistent storage of documentation, knowledge, and cache data.

  • Offers integration for issue tracking and community discussions through GitHub repositories, supporting user feedback and problem resolution workflows.

MCP Codebase Insight - WIP

🚧 Development in Progress

This project is actively under development. Features and documentation are being continuously updated.

Overview

MCP Codebase Insight is a system for analyzing and understanding codebases through semantic analysis, pattern detection, and documentation management.

Current Development Status

Completed Features

  • ✅ Core Vector Store System
  • ✅ Basic Knowledge Base
  • ✅ SSE Integration
  • ✅ Testing Framework
  • ✅ TDD and Debugging Framework (rules_template integration)

In Progress

  • 🔄 Documentation Management System
  • 🔄 Advanced Pattern Detection
  • 🔄 Performance Optimization
  • 🔄 Integration Testing
  • 🔄 Debugging Utilities Enhancement

Planned

  • 📋 Extended API Documentation
  • 📋 Custom Pattern Plugins
  • 📋 Advanced Caching Strategies
  • 📋 Deployment Guides
  • 📋 Comprehensive Error Tracking System

Quick Start

  1. Installation
    pip install mcp-codebase-insight
  2. Basic Usage
    from mcp_codebase_insight import CodebaseAnalyzer analyzer = CodebaseAnalyzer() results = analyzer.analyze_code("path/to/code")
  3. Running Tests
    # Run all tests pytest tests/ # Run unit tests pytest tests/unit/ # Run component tests pytest tests/components/ # Run tests with coverage pytest tests/ --cov=src --cov-report=term-missing
  4. Debugging Utilities
    from mcp_codebase_insight.utils.debug_utils import debug_trace, DebugContext, get_error_tracker # Use debug trace decorator @debug_trace def my_function(): # Implementation # Use debug context with DebugContext("operation_name"): # Code to debug # Track errors try: # Risky operation except Exception as e: error_id = get_error_tracker().record_error(e, context={"operation": "description"}) print(f"Error recorded with ID: {error_id}")

Testing and Debugging

Test-Driven Development

This project follows Test-Driven Development (TDD) principles:

  1. Write a failing test first (Red)
  2. Write minimal code to make the test pass (Green)
  3. Refactor for clean code while keeping tests passing (Refactor)

Our TDD documentation can be found in docs/tdd/workflow.md.

Debugging Framework

We use Agans' 9 Rules of Debugging:

  1. Understand the System
  2. Make It Fail
  3. Quit Thinking and Look
  4. Divide and Conquer
  5. Change One Thing at a Time
  6. Keep an Audit Trail
  7. Check the Plug
  8. Get a Fresh View
  9. If You Didn't Fix It, It Isn't Fixed

Learn more about our debugging approach in docs/debuggers/agans_9_rules.md.

Documentation

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

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

Support

-
security - not tested
F
license - not found
-
quality - not tested

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

  1. Overview
    1. Current Development Status
      1. Completed Features
      2. In Progress
      3. Planned
    2. Quick Start
      1. Testing and Debugging
        1. Test-Driven Development
        2. Debugging Framework
      2. Documentation
        1. Contributing
          1. License
            1. Support

              Related MCP Servers

              • A
                security
                A
                license
                A
                quality
                A Model Context Protocol (MCP) server that provides code analysis capabilities using tree-sitter, designed to give Claude intelligent access to codebases with appropriate context management.
                Last updated -
                26
                33
                Python
                MIT License
                • Apple
                • Linux
              • -
                security
                A
                license
                -
                quality
                A Model Context Protocol server that enables IDEs like Cursor and Windsurf to analyze large codebases using Gemini's extensive context window.
                Last updated -
                17
                Python
                MIT License
              • -
                security
                F
                license
                -
                quality
                A comprehensive Model Context Protocol server for advanced code analysis that provides tools for syntax analysis, dependency visualization, and AI-assisted development workflow support.
                Last updated -
                2
                Python
              • A
                security
                A
                license
                A
                quality
                A Model Context Protocol server that helps large language models process code repositories by providing file tree generation, code merging, and code analysis capabilities.
                Last updated -
                3
                14
                JavaScript
                MIT License

              View all related MCP servers

              ID: xjd6z5tuj8