What is Mcp Dev Agent

mcp-dev-agent is a development agent based on the Model Context Protocol (MCP) that provides memory, documentation, and Git integration functionalities, available in both CLI mode and as a Server-Sent Events (SSE) server.

Use cases

Use cases include managing project documentation, analyzing code quality, tracking Git commit history, and facilitating collaborative development through GitHub integration.

How to use

To use mcp-dev-agent, clone the repository, install dependencies, configure environment variables, and set up code parsers. It can be run in CLI mode or as a server, with various commands available for memory management and Git operations.

Key features

Key features include a memory system that stores and retrieves information using embeddings via ChromaDB, documentation search capabilities, Git integration for commit history and issues, static code analysis using tree-sitter, and an interactive CLI interface.

Where to use

mcp-dev-agent can be used in software development environments, particularly for teams that require efficient documentation management, code analysis, and Git integration.

MCP Development Agent

A development agent based on the Model Context Protocol (MCP) that provides memory management, documentation search, and Git/GitHub integration features, available both as a CLI tool and as an SSE server.

Features

• 🧠 Memory System: Store and retrieve information using embeddings via ChromaDB
• 📚 Documentation Search: Search through documentation with AI assistance
• 🔄 Git Integration: Query commit history and issues
• 🔍 Code Analysis: Static code analysis using tree-sitter
• 🌐 GitHub Integration: Search repositories, issues, pull requests, projects, and code
• 💡 AI Assistant: Local AI-powered features using Ollama
• 💻 CLI Interface: Interactive command-line interface
• 🌐 Server Mode: Server-Sent Events (SSE) support

Requirements

• Python 3.x
• ChromaDB
• GitPython
• FastMCP
• PyGithub
• tree-sitter
• Ollama (https://ollama.ai)

Installation

Global Installation (Recommended)

1. Install Ollama following instructions at https://ollama.ai

2. Pull required models:

ollama pull codellama
ollama pull llama3
ollama pull mistral

3. Clone and install:

For Windows:

git clone [repository-url]
cd mcp-dev-agent
.\install.bat

For Linux/MacOS:

git clone [repository-url]
cd mcp-dev-agent
./install.sh

This will:

• Create a virtual environment
• Install all dependencies
• Configure the global command mcp-dev-agent-cli
• Set up the required directory structure

4. Configure your GitHub token in the .env file

After installation, you can use the mcp-dev-agent-cli command from any git repository!

Manual Installation (Alternative)

1. Install Ollama following instructions at https://ollama.ai

2. Pull required models:

ollama pull codellama
ollama pull llama3
ollama pull mistral

3. Clone the repository:

git clone [repository-url]
cd mcp-dev-agent

4. Install dependencies:

pip install -r requirements.txt

5. Configure environment variables (copy .env.example to .env):

cp .env.example .env

Setting up GitHub Token

1. Go to GitHub Personal Access Tokens
2. Click “Generate new token (classic)”
3. Give it a name (e.g., “MCP Dev Agent”)
4. Select these scopes:
   • repo (Full control of repositories)
   • read:org (Read organization info)
5. Click “Generate token”
6. IMPORTANT: Copy your token immediately! You won’t see it again.
7. Add it to your .env file:

   GITHUB_TOKEN=ghp_your_actual_token_here
   

8. Or set it in your environment:

   export GITHUB_TOKEN=ghp_your_actual_token_here
   

Note: Replace ghp_your_actual_token_here with the actual token you copied from GitHub.

7. Set up code parsers:

python setup_parsers.py

Usage

Global CLI

The MCP Dev Agent can be used from any git repository using the global command:

mcp-dev-agent-cli

This command will:

• Automatically set up required directories in your project
• Use the correct Python environment
• Maintain proper context isolation between projects

Traditional CLI Mode

python main.py --mode cli

Available commands:

Memory

• /memory add <content> - Add a new general memory
• /memory get <query> - Search existing memories
• /memory repo add <content> - Add a repository-specific memory
• /memory repo get <query> - Search repository-specific memories

Git and GitHub

• /git commits [number] - Show commit history (default: last 5)
• /git issues - List local repository issues
• /git info - Show detailed repository information
• /git diff - Show pending changes (staged and unstaged)
• /github repo <owner/repo> - Show repository details
• /github issues <owner/repo> [state] - List issues (state: open/closed)
• /github prs <owner/repo> [state] - List pull requests (state: open/closed)
• /github project <org> <number> - Show project information
• /github summarize <owner/repo> <issue_number> - Generate issue summary using GPT
• /github search <query> [language] - Search code on GitHub

Code Analysis

• /code analyze <file> [language] - Analyze code structure and provides:
  • High-level overview of the code’s purpose
  • Key components and their responsibilities
  • Notable patterns or techniques used
  • Potential improvements and best practices
  • Security considerations when relevant
  • Structural analysis (functions, classes, imports)

Example:

mcp-dev-agent-cli
> /code analyze src/main.py

Documentation

• /docs <query> - Search documentation
• exit - Exit CLI mode

Advanced Features

Code Analysis

The system uses tree-sitter to analyze code in:

• Python
• JavaScript
• TypeScript

Analysis includes:

• Function and method extraction
• Class identification
• Import mapping
• Memory integration for context

GitHub Integration

• Detailed repository information
• Issue search and listing
• Pull request management
• GitHub Projects integration
• Code search with language filtering
• Enriched metadata
• Automatic issue summarization with GPT

Enhanced Memory System

• Support for different context types
• Enriched metadata
• Contextual search
• Temporal history
• Code analysis integration
• Automatic summary storage
• Git state contextualization

Server Mode (SSE)

python main.py
# or
python main.py --mode server

The SSE server enables integration with other applications through the MCP protocol.

Project Structure

.
├── main.py              # Application entry point
├── setup_parsers.py     # Code parser configuration
├── tools/
│   ├── memory_tool.py   # Memory management via ChromaDB
│   ├── doc_tool.py      # Documentation search
│   ├── git_tool.py      # Git integration
│   └── github_tool.py   # GitHub integration and code analysis
└── docs/
    └── api_reference.md # API reference documentation

AI Model Configuration

The agent uses Ollama’s local models for AI features. The following models are configured:

1. CodeLlama (default) - Optimized for code-related tasks
2. Llama 2 - General purpose tasks
3. Mistral - Alternative model for various tasks

The .agent.json file is already configured with:

• CodeLlama as default model
• Response caching (24 hours)
• Interaction logging

Model Parameters

You can adjust model parameters by editing the .agent.json file:

• temperature: Controls response creativity (0.0 to 1.0)
• context_window: Maximum context length
• top_p: Controls response diversity

Using Different Models

Different features use specialized models:

• Code documentation: CodeLlama
• Issue summarization: CodeLlama
• General queries: Llama 2

No API keys required - all AI features run locally through Ollama!