MCP Codebase Insight

by tosin2013
Verified

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

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

🚧 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

In Progress

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

Planned

  • 📋 Extended API Documentation
  • 📋 Custom Pattern Plugins
  • 📋 Advanced Caching Strategies
  • 📋 Deployment Guides

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
    pytest tests/

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

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security - not tested
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license - not found
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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. Documentation
        1. Contributing
          1. License
            1. Support
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