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.
Enables configuration of the MCP server through environment variables loaded from .env files, simplifying deployment setup and configuration management.
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
Installation
pip install mcp-codebase-insightBasic Usage
from mcp_codebase_insight import CodebaseAnalyzer analyzer = CodebaseAnalyzer() results = analyzer.analyze_code("path/to/code")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-missingDebugging 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:
Write a failing test first (Red)
Write minimal code to make the test pass (Green)
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:
Understand the System
Make It Fail
Quit Thinking and Look
Divide and Conquer
Change One Thing at a Time
Keep an Audit Trail
Check the Plug
Get a Fresh View
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
This server cannot be installed
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.
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