codebase-mcp by danyQe - MCP Server

Codebase MCP

Privacy-first AI development assistant via MCP | Turn Claude into your personal coding assistant | Semantic code search • AI-assisted editing • Quality-checked generation • Persistent memory | Free open-source alternative to Cursor & paid AI coding tools | Python, React, TypeScript, FastAPI

Open Source**

Codebase MCP is an open-source AI-powered development assistant that connects Claude Desktop (or any MCP-compatible LLM) to your codebase through the Model Context Protocol. Stop paying for separate coding assistants - if you already have a Claude subscription, that's all you need.

📖 Read Full Documentation | 🏗️ | 🤝

🌟 Why Codebase MCP?

The Problem

Modern AI coding assistants like Cursor, Windsurf, and others charge $20-40+/month on top of your existing LLM subscription. If you already pay for Claude, why pay again for a coding assistant?

The Solution

Codebase MCP turns your existing Claude subscription into a powerful coding assistant:

✅ One subscription - Use your existing Claude Pro/Team plan
✅ Privacy-first - Local embeddings and processing (except edit operations)
✅ Open source - Apache 2.0 license, community-driven
✅ Extensible - Works with any MCP-compatible LLM via Model Context Protocol
✅ Lightweight - ~100MB memory footprint for medium projects
✅ Fast - Sub-second semantic search with local FAISS indexing

⚡ Quick Start

Prerequisites

Python 3.11+
Claude Desktop (or any MCP-compatible client)
Git installed
uv package manager (recommended)

Installation

1. Clone the repository:

git clone https://github.com/danyQe/codebase-mcp.git
cd codebase-mcp

2. Install globally (recommended):

# Install uv if you haven't
pip install uv

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt

# Install formatters globally (required for code formatting)
pip install black ruff

3. Configure Gemini API (for edit tool):

# Create .env file
cp .env.example .env

# Get free API key from: https://aistudio.google.com/app/apikey
# Add to .env:
GEMINI_API_KEY=your_api_key_here

4. Configure Claude Desktop:

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "codebase-manager": {
      "command": "/path/to/your/.venv/bin/python",
      "args": [
        "/path/to/codebase-mcp/mcp_server.py"
      ]
    }
  }
}

5. Start the FastAPI server:

# In a separate terminal, navigate to your project directory
python main.py /path/to/your/project

# Server starts on http://localhost:6789
# You can access the web dashboard at: http://localhost:6789

6. Use with Claude Desktop:

Restart Claude Desktop
Start chatting - Claude now has access to 13+ MCP tools for codebase management!

🎯 Key Features

🔍 Semantic Code Search

AI-powered code understanding with local embeddings
Multi-language support (Python, JavaScript, TypeScript)
Symbol-level indexing (functions, classes, interfaces)
Fuzzy search and exact matching modes

🧠 Persistent Memory System

Remember context across chat sessions
Categorize learnings: progress, mistakes, solutions, architecture
Semantic memory search with importance scoring
Never repeat the same mistake twice

🌿 Session-Based Git Workflow

Isolated development branches for each feature
Automatic commit tracking in .codebase directory
Separate from user's .git - track AI changes independently
Auto-merge support with quality gates

✍️ Intelligent Code Writing

Write Tool: Create new files with auto-formatting and quality scoring
Edit Tool: AI-assisted editing with Gemini integration (inspired by Cursor)
Quality Gates: Auto-commit only when code quality ≥ 80%
Dependency Checking: Prevent code duplication and missing imports

🎨 Auto-Formatting

Python: Black + Ruff (PEP 8 compliant)
TypeScript/JavaScript: Prettier + ESLint
Quality Scoring: Automatic code quality assessment
Error Recovery: Intelligent retry with corrections

📊 Project Intelligence

Real-time codebase analysis
File structure visualization
Dependency tracking
Symbol extraction and indexing

🏗️ Architecture

┌─────────────────┐
│ Claude Desktop  │  User interacts via chat
└────────┬────────┘
         │ MCP Protocol (stdio)
         ↓
┌─────────────────┐
│   MCP Server    │  13+ Tools (proxy layer)
│  (mcp_server.py)│  Lightweight, fast
└────────┬────────┘
         │ HTTP/REST
         ↓
┌─────────────────┐
│ FastAPI Server  │  Port 6789 (main.py)
│   Core Engine   │  40+ API endpoints
└────────┬────────┘
         │
    ┌────┴─────┬─────────┬──────────┐
    ↓          ↓         ↓          ↓
┌────────┐ ┌──────┐ ┌────────┐ ┌───────────┐
│Semantic│ │Memory│ │  Git   │ │Code Tools │
│ Search │ │System│ │Manager │ │  Pipeline │
└────────┘ └──────┘ └────────┘ └───────────┘
    │          │         │          │
    └──────────┴─────────┴──────────┘
               ↓
    ┌──────────────────────┐
    │   Local Storage      │
    │ • FAISS (vectors)    │
    │ • SQLite (metadata)  │
    │ • .codebase (git)    │
    └──────────────────────┘

Privacy Note: All processing is local except the edit tool, which uses Google's free Gemini API for AI-assisted code editing. Only the edited file is sent to Gemini - no project context or history.

🛠️ Available Tools

Codebase MCP provides 13 specialized MCP tools for comprehensive development automation:

Tool	Purpose	Key Features
`session_tool`	Manage dev sessions	Create branches, auto-commit, merge
`memory_tool`	Store/retrieve knowledge	Persistent context, semantic search
`git_tool`	Git operations	Status, diff, log, commit, branches
`write_tool`	Intelligent file creation	Auto-format, quality scoring, dependency check
`edit_file`	AI-assisted editing	Gemini-powered, error recovery, format validation
`search_tool`	Semantic code search	4 modes: semantic, fuzzy, text, symbol
`read_code_tool`	Smart code reading	Symbol-level, line ranges, whole file
`project_context_tool`	Project analysis	Structure, dependencies, overview
`list_directory_tool`	Directory exploration	Tree view, metadata, gitignore support
`code_analysis_tool`	Code quality checks	Syntax, linting, imports, dependencies
`list_file_symbols_tool`	Symbol extraction	Functions, classes, interfaces
`read_symbol_from_database`	DB symbol lookup	Fast indexed retrieval
`project_structure_tool`	Project visualization	Enhanced tree with stats

📈 Performance

Semantic Search: Sub-second response for typical codebases
Memory Footprint: ~100MB for medium projects (<20k lines)
Indexing Speed: ~30 seconds for 10k lines initial index
Edit Operations: 5-15 seconds (Gemini API + formatting)
Optimal Project Size: <20,000 lines (tested and verified)

Note: Edit tool can be slow due to Gemini API latency and code formatting. Claude Desktop may timeout on very large edits (use smaller, focused edits).

🎓 Usage Examples

Creating a New Feature

User: "Create a FastAPI endpoint for user authentication with JWT tokens"

Claude:
✅ Searching for existing auth patterns...
✅ Creating session: feat/user-auth
✅ Writing authentication.py with JWT implementation
✅ Auto-formatted with Black + Ruff
✅ Quality score: 95% - Auto-committed
✅ Storing solution in memory

Refactoring Code

User: "Refactor the user service to use dependency injection"

Claude:
✅ Reading current user service implementation
✅ Searching for DI patterns in codebase
✅ Editing with AI assistance (Gemini)
✅ Validating changes with quality gates
✅ Session: refactor/user-di ready for review

Memory-Driven Development

User: "Continue working on the payment integration"

Claude:
✅ Loading memory context...
✅ Found previous progress: Stripe API setup complete
✅ Found previous mistake: Don't use synchronous requests in async endpoints
✅ Continuing from last checkpoint...

🔐 Privacy & Security

Privacy-First Design

Local Embeddings: AllMiniLM-L6-v2 runs entirely on your machine
Local Processing: FAISS vector store, SQLite metadata - all local
No Cloud Dependencies: Except for Gemini API (edit tool only)

Gemini API Usage

Scope: Only edit_file tool uses Gemini
Data Sent: Only the file being edited (no project context)
Alternative: Contributors can add local LLM support (GPU required)
Cost: Free tier (15 RPM, 250K TPM, 1K RPD)

Security Best Practices

Never commit .env with API keys
Use .gitignore for sensitive files
Review AI-generated code before production deployment
Keep dependencies updated

🤝 Contributing

We welcome contributions! This project was built to be community-driven and extensible.

Priority Areas:

🌐 Language Support: Add Java, Go, Rust, PHP, etc.
🧠 Local LLM Integration: Replace Gemini with local models
🔍 Search Improvements: Enhanced semantic algorithms
📊 UI/UX: Improve web dashboard
⚡ Performance: Optimization for larger codebases

See for detailed guidelines.

📄 License

This project is licensed under the Apache License 2.0 - see file for details.

Credits:

Edit tool techniques inspired by Cursor
Built with Claude Sonnet 4 and 4.5
Powered by Model Context Protocol

🗺️ Roadmap

Current Version: v1.0.0-beta

Upcoming Features:

Community-driven enhancements
More language support
Local LLM alternatives
Performance optimizations
Advanced prompt engineering templates

📞 Support

📖 Documentation: https://danyqe.github.io/codebase-mcp/
🐛 Issues: https://github.com/danyQe/codebase-mcp/issues
💬 Discussions: https://github.com/danyQe/codebase-mcp/discussions
🌟 Star this repo if you find it useful!

🎉 Acknowledgments

Special thanks to:

Anthropic for Claude and the Model Context Protocol
Google for the free Gemini API
Cursor team for pioneering AI-assisted editing techniques
Open source community for making this possible

Made with ❤️ by developers, for developers

Stop paying for coding assistants. Start building with your own LLM.