DevAssist MCP Server

DevAssist MCP Server

A production-ready MCP server for GitHub and competitive programming (Codeforces) that enables AI assistants to fetch user profiles, repository stats, contest history, and personalized problem recommendations.

Category
访问服务器

README

🚀 DevAssist MCP Server

A production-ready Python MCP Server for GitHub & Competitive Programming

Python MCP Render License Docker


🌐 Live Cloud Deployment: DevAssist MCP Server is deployed and live on Render.com!

  • Base URL & Health Check: https://devassist-mcp-server.onrender.com/
  • SSE Endpoint: https://devassist-mcp-server.onrender.com/sse

You can connect your Claude Desktop, Cursor, or VS Code client directly to the hosted Render server without running code locally, or run it locally on your own machine.


📖 Overview & Project Description

DevAssist MCP Server is a production-grade, asynchronous Model Context Protocol (MCP) server engineered to connect AI assistants—such as Claude Desktop, Cursor, and VS Code Copilot—with real-time developer workflows, GitHub repository intelligence, and competitive programming analytics.

🎯 Purpose & Core Capabilities

Modern AI coding agents often lack direct access to external developer platforms and competitive programming metrics. DevAssist MCP addresses this by providing two dedicated tools through standard MCP protocol interfaces (STDIO and Server-Sent Events / SSE):

  1. 🐙 github_assistant: Provides comprehensive GitHub integration including user profile lookups, repository metadata, commit histories, code language composition breakdowns, active pull requests, issue tracking, contributor metrics, and release releases notes.
  2. 🏆 cp_assistant: Integrates with Codeforces to fetch user contest ratings, submission performance history, topic-based accuracy tracking, algorithmic weak-spot identification, and personalized practice problem recommendations based on rating level and weak tags.

Whether deployed locally or accessed via its live Render.com cloud endpoint (https://devassist-mcp-server.onrender.com/sse), DevAssist MCP enables LLMs to reason over live developer data dynamically.

✨ Key Features

Feature Description
🌐 Cloud Deployed Live hosting on Render.com with full Server-Sent Events (SSE) support
🐙 GitHub Integration User profiles, repo info, commits, PRs, issues, statistics
🏆 Codeforces Integration Profiles, contest history, rating tracking, submissions
🧠 Weak Topic Analysis AI-powered identification of competitive programming weak areas
💡 Smart Recommendations Personalized problem suggestions based on skill gaps
⚡ Async Architecture Non-blocking I/O with httpx for maximum performance
🔒 Production Ready Logging, error handling, type safety, comprehensive tests

🏗️ Architecture

graph TD
    A["🤖 AI Agent<br/>(Claude / Cursor / VS Code)"] -->|MCP Protocol / SSE| B["🌐 Live Render MCP Server / Local Server"]
    B --> C["🐙 github_assistant"]
    B --> D["🏆 cp_assistant"]
    C --> E["GitHub Service Layer"]
    D --> F["Codeforces Service Layer"]
    E -->|httpx async| G["GitHub REST API v3"]
    F -->|httpx async| H["Codeforces API"]
    
    style A fill:#4A90D9,stroke:#2C5F99,color:#fff
    style B fill:#6C3483,stroke:#4A235A,color:#fff
    style C fill:#27AE60,stroke:#1E8449,color:#fff
    style D fill:#E67E22,stroke:#CA6F1E,color:#fff

🔌 Connecting to Claude Desktop (claude_desktop_config.json)

To use this MCP server in Claude Desktop, open or create your claude_desktop_config.json file.

📍 File Locations:

  • Windows: %APPDATA%\Claude\claude_desktop_config.json (e.g., C:\Users\<username>\AppData\Roaming\Claude\claude_desktop_config.json)
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

Option A: Use Live Render Deployment (Recommended)

Connect directly to the cloud-hosted server on Render.com using mcp-remote bridge:

{
  "mcpServers": {
    "devassist-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://devassist-mcp-server.onrender.com/sse"
      ]
    }
  }
}

Or if your version of Claude Desktop supports native SSE configuration:

{
  "mcpServers": {
    "devassist-mcp": {
      "url": "https://devassist-mcp-server.onrender.com/sse"
    }
  }
}

Option B: Use Local Machine Execution

If you prefer to run the MCP server locally on your own machine:

{
  "mcpServers": {
    "devassist-mcp": {
      "command": "python",
      "args": ["C:/path/to/devassist-mcp/server.py"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

Note: Replace C:/path/to/devassist-mcp/server.py with the absolute path to server.py on your machine.


💻 Other IDE Configurations

Cursor

Add to your Cursor MCP settings (.cursor/mcp.json):

Using Render Cloud Endpoint:

{
  "mcpServers": {
    "devassist-mcp": {
      "url": "https://devassist-mcp-server.onrender.com/sse"
    }
  }
}

Using Local Python Script:

{
  "mcpServers": {
    "devassist-mcp": {
      "command": "python",
      "args": ["C:/path/to/devassist-mcp/server.py"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

VS Code (with Copilot)

Add to your VS Code settings:

{
  "mcp": {
    "servers": {
      "devassist-mcp": {
        "command": "npx",
        "args": [
          "-y",
          "mcp-remote",
          "https://devassist-mcp-server.onrender.com/sse"
        ]
      }
    }
  }
}

📋 Prerequisites & Local Setup

Prerequisites

  • Python 3.12+
  • GitHub Personal Access Token (Generate here) — for higher rate limits
  • pip or uv package manager

Option 1: Using uv (Recommended)

# Clone the repository
git clone https://github.com/Champion-2006/DevAssist-MCP-Server.git
cd DevAssist-MCP-Server

# Create virtual environment and install dependencies
uv venv
uv pip install -r requirements.txt

Option 2: Using pip

# Clone the repository
git clone https://github.com/Champion-2006/DevAssist-MCP-Server.git
cd DevAssist-MCP-Server

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

⚙️ Local Configuration

  1. Copy the environment template:

    cp .env.example .env
    
  2. Edit .env with your settings:

    GITHUB_TOKEN=ghp_your_personal_access_token_here
    LOG_LEVEL=INFO
    TRANSPORT=stdio
    PORT=8000
    

Note: The GitHub token is optional but recommended. Without it, requests are limited to 60 API calls/hour. With a token, limit increases to 5,000 requests/hour.


🚀 Running Local Standalone

STDIO Mode (Default)

python server.py

SSE Transport Mode

python server.py --sse
# or
TRANSPORT=sse PORT=8000 python server.py

🛠️ Tool Documentation

Tool 1: github_assistant

Action Description Requires repository
get_user_profile Get GitHub user profile ❌
get_user_repos List all public repositories owned by a user ❌
get_user_activity Get recent user activity (commits, PRs, stars) ❌
get_repo_info Get repository details ✅
get_latest_commits Get recent commits ✅
get_repo_stats Get repository statistics ✅
get_repo_languages Detailed programming language percentage breakdown ✅
get_repo_contributors Top code contributors to a repository ✅
get_latest_release Latest release version & release notes ✅
get_pull_requests Get pull requests ✅
get_issues Get repository issues ✅

Example — Get User Profile:

"Show me the GitHub profile of octocat"
{
  "login": "octocat",
  "name": "The Octocat",
  "public_repos": 42,
  "followers": 20000,
  "location": "San Francisco"
}

Example — Get Repo Stats:

"What are the stats for octocat/Hello-World?"
{
  "repository": "octocat/Hello-World",
  "stars": 2500,
  "forks": 450,
  "language": "Python",
  "open_issues": 12
}

Tool 2: cp_assistant

Action Description
get_user_profile Get Codeforces rating and rank
get_contest_history Past contest participation
get_recent_submissions Recent problem submissions
get_rating_history Rating changes over time
analyze_weak_topics Identify weak problem areas
recommend_problems Personalized problem suggestions

Example — Analyze Weak Topics:

"Analyze the weak topics for Codeforces user tourist"
[
  {
    "tag": "geometry",
    "total_attempts": 15,
    "successful": 5,
    "failed": 10,
    "success_rate": 33.3
  }
]

Example — Get Recommendations:

"Recommend practice problems for my Codeforces handle"
[
  {
    "name": "Theatre Square",
    "rating": 1000,
    "tags": ["math"],
    "url": "https://codeforces.com/problemset/problem/1/A"
  }
]

🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=. --cov-report=term-missing

# Run specific test file
pytest tests/test_github_service.py -v

# Run specific test
pytest tests/test_codeforces_service.py::test_analyze_weak_topics -v

📁 Project Structure

devassist-mcp/
├── server.py                          # MCP server entry point (STDIO & SSE)
├── config.py                          # Pydantic settings
├── .env.example                       # Environment template
├── requirements.txt                   # Dependencies
├── pyproject.toml                     # Project metadata & tool configs
├── Dockerfile                         # Multi-stage Docker build
├── .dockerignore                      # Docker build exclusions
├── .gitignore                         # Git exclusions
├── README.md                          # Comprehensive documentation
├── tools/
│   ├── github.py                      # GitHub MCP tool
│   └── cp.py                          # Codeforces MCP tool
├── services/
│   ├── github_service.py              # GitHub API client
│   └── codeforces_service.py          # Codeforces API client
├── models/
│   ├── github_models.py               # GitHub Pydantic models
│   └── cp_models.py                   # Codeforces Pydantic models
├── utils/
│   ├── logger.py                      # Structured logging
│   └── exceptions.py                  # Custom exception hierarchy
├── tests/
│   ├── conftest.py                    # Shared test fixtures
│   ├── test_github_service.py         # GitHub service tests
│   ├── test_codeforces_service.py     # Codeforces service tests
│   ├── test_github_tool.py            # GitHub tool tests
│   └── test_cp_tool.py               # Codeforces tool tests
└── logs/                              # Log files (auto-created)

🐳 Deployment Guide

Render Deployment (Live Production)

This project is deployed live on Render.com as a Web Service running in SSE transport mode:

  1. Service Type: Web Service
  2. Build Command: pip install -r requirements.txt
  3. Start Command: python server.py --sse
  4. Environment Variables:
    • TRANSPORT: sse
    • PORT: 10000 (or Render default)
    • GITHUB_TOKEN: ghp_your_github_token
  5. Live URL: https://devassist-mcp-server.onrender.com

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Commit changes: git commit -m 'Add amazing feature'
  4. Push to branch: git push origin feature/amazing-feature
  5. Open a Pull Request

Code Quality

# Format code
black .

# Lint
ruff check .

# Type check
mypy .

<div align="center">

Built with ❤️ using Python, FastMCP, and httpx

</div>

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选