Amannpy/devpilot-mcp

DevPilot MCP : An Intelligent Developer Workflow MCP Server

An AI-powered Model Context Protocol (MCP) server designed to enhance software development workflows through intelligent code review, automated documentation, bug detection, complexity analysis, and test generation. It integrates Qwen2.5 and other Hugging Face models for high-quality AI assistance in development processes.

Key Features

MCP Tools

• Code Review Automation – AI-based pull request analysis with actionable feedback
• Bug Detection – Identifies vulnerabilities, logic issues, and common anti-patterns
• Documentation Generation – Automatically produces structured technical documentation
• Complexity Analysis – Scores code complexity and suggests refactoring options
• Test Generation – Generates unit tests using preferred testing frameworks

MCP Resources

• Git repository and project health analysis
• Code quality metrics and insights
• Optional integration with issue tracking systems

AI Models Used

• Qwen2.5 – Advanced code understanding and generation
• CodeBERT – Code embedding generation
• FLAN-T5 – Natural language generation and summarization

Project Structure

devpilot-mcp/
├── src/
│   ├── server.py          # Core MCP server
│   ├── config.py          # Configuration and environment settings
│   ├── tools.py           # MCP tool implementations
│   ├── resources.py       # Resource definitions
│   └── models.py          # AI model integration logic
├── tests/
│   ├── test_server.py
│   ├── test_model.py
│   └── test_tools.py
├── demo.py                # Example runner for local testing
├── requirements.txt
├── pyproject.toml
├── .env.example
└── README.md

Quick Start

Prerequisites

• Python 3.10 or higher
• Git
• (Optional) Hugging Face API token for extended rate limits

Installation

git clone https://github.com/amannpy/devpilot-mcp.git
cd devpilot-mcp
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env

Then edit .env and set your configuration values if needed.

Running the Server

python src/server.py

To try the demo script:

python demo.py

Usage Examples

Example 1: Code Review

{
  "tool": "review_pull_request",
  "arguments": {
    "pr_content": "def calculate(a, b): return a + b",
    "language": "python"
  }
}

Example 2: Bug Detection

{
  "tool": "detect_bugs",
  "arguments": {
    "code_content": "query = 'SELECT * FROM users WHERE id = ' + user_id",
    "severity_filter": "critical"
  }
}

Example 3: Complexity Analysis

{
  "tool": "analyze_complexity",
  "arguments": {
    "code_content": "def f():\n    for i in range(10):\n        if i % 2 == 0:\n            print(i)"
  }
}

Configuration

Environment Variables

Example .env:

HUGGINGFACE_API_TOKEN=hf_xxxxxxxxxxxxxx
LOG_LEVEL=INFO
MCP_SERVER_NAME=intelligent-dev-workflow
MAX_FILE_SIZE=100000
MAX_COMPLEXITY_SCORE=10.0

Advanced Settings

Edit src/config.py to customize:

• Model paths and APIs
• Cache strategy and expiration
• Logging and verbosity
• Complexity thresholds

Testing

Run all test cases:

pytest -v

With coverage:

pytest --cov=src --cov-report=html

Individual test file:

pytest tests/test_server.py -v

Type checking with mypy:

mypy src/ --ignore-missing-imports

Linting with ruff:

ruff check
ruff check --fix  # Auto-fix issues

MCP Integration

Example configuration for an MCP client:

{
  "mcpServers": {
    "intelligent-dev-workflow": {
      "command": "python",
      "args": ["src/server.py"],
      "env": {
        "HUGGINGFACE_API_TOKEN": "your_token_here"
      }
    }
  }
}

Available Tools

Performance and Design

• Caching: In-memory caching with configurable TTL
• Asynchronous Processing: Non-blocking async I/O using asyncio
• Rate Limiting: Adaptive throttling for API usage
• Logging: Structured JSON and console logging options

Development Guidelines

• Follows PEP 8 coding standards
• Uses type hints throughout (mypy compatible)
• Includes unit tests for all core modules
• Well-documented, modular architecture

To contribute:

git checkout -b feature/your-feature
git commit -m "Add new feature"
git push origin feature/your-feature

Then open a Pull Request.

License

Licensed under the MIT License.

Contact

• GitHub Issues: Open an issue
• Discussions: Join discussion
• Email: aman.kumar.cse2611@gmail.com

Roadmap

• GitHub Actions CI/CD enhancements
• VS Code and JetBrains plugin integration
• Real-time web dashboard
• Expanded multi-language model support
• SaaS deployment template

Developed for modern developers seeking to integrate AI intelligence into their workflow.