mcp-github-agent
Enables AI assistants to query GitHub data directly through natural language, including user profiles, repositories, issues, and search.
README
mcp-agent
A multi-service MCP (Model Context Protocol) server that gives an AI agent access to 34 tools across 9 platforms — GitHub, Jira, Azure DevOps, Slack, PagerDuty, Linear, Notion, HuggingFace, and OpenWeather.
Instead of switching between tabs and dashboards, you talk to your AI assistant in plain English and it queries or acts on these systems directly.
Works with Claude, GPT-4o, Azure OpenAI, and any MCP-compatible client.
What is MCP?
The Model Context Protocol is an open standard by Anthropic that lets AI models communicate with external tools and APIs in a structured, secure way. The AI stays in the conversation but can reach out to real systems to fetch data or take actions — autonomously deciding which tools to call and in what order.
Services & Tools
GitHub
| Tool | Type | What it does |
|---|---|---|
get_user_profile |
read | Bio, location, followers, and public repo count |
get_user_repos |
read | Public repositories sorted by activity, stars, or date |
get_repo_info |
read | Stars, forks, open issues, topics, license, last push |
get_repo_issues |
read | Open or closed issues |
get_pull_requests |
read | Open or merged pull requests |
get_file_content |
read | Content of any file in a repository |
get_repo_contributors |
read | Top contributors ranked by commit count |
get_repo_releases |
read | Latest releases and changelogs |
get_trending |
read | Trending repos by language and period (daily/weekly/monthly) |
search_repos |
read | Search by keyword, topic, or language |
create_issue |
write | Create a new issue (requires repo token scope) |
Jira
| Tool | Type | What it does |
|---|---|---|
jira_search_issues |
read | Search issues using JQL |
jira_get_issue |
read | Full details of an issue by key |
jira_get_project_issues |
read | Issues for a project filtered by status |
jira_create_issue |
write | Create a Task, Bug, Story, or Epic |
Azure DevOps
| Tool | Type | What it does |
|---|---|---|
ado_list_pipelines |
read | List CI/CD pipelines in a project |
ado_get_pipeline_runs |
read | Recent runs for a pipeline |
ado_search_work_items |
read | Search work items by keyword |
ado_create_work_item |
write | Create a Task, Bug, User Story, or Epic |
Slack
| Tool | Type | What it does |
|---|---|---|
slack_get_channels |
read | List public channels in the workspace |
slack_get_messages |
read | Read recent messages from a channel |
slack_send_message |
write | Send a message to a channel or user |
PagerDuty
| Tool | Type | What it does |
|---|---|---|
pagerduty_get_incidents |
read | List incidents by status |
pagerduty_get_services |
read | List configured services |
pagerduty_create_incident |
write | Trigger a new incident |
Linear
| Tool | Type | What it does |
|---|---|---|
linear_get_teams |
read | List teams in the workspace |
linear_get_issues |
read | Issues for a team filtered by state |
linear_create_issue |
write | Create an issue in a team |
Notion
| Tool | Type | What it does |
|---|---|---|
notion_search |
read | Search pages and databases by keyword |
notion_get_page |
read | Get the content of a page |
notion_create_page |
write | Create a new page under a parent |
HuggingFace
| Tool | Type | What it does |
|---|---|---|
hf_search_models |
read | Search models by keyword and task |
hf_get_model |
read | Detailed model info (downloads, likes, tags) |
hf_search_datasets |
read | Search datasets by keyword |
OpenWeather
| Tool | Type | What it does |
|---|---|---|
weather_current |
read | Current weather for a city |
weather_forecast |
read | Multi-day forecast for a city |
Example use cases
Cross-service reasoning
"Check if there are any open Jira bugs related to our Azure DevOps pipeline failures this week, then create a GitHub issue summarizing them."
The agent calls jira_search_issues → ado_get_pipeline_runs → create_issue autonomously.
Incident response
"There's an active PagerDuty incident on the payments service. Find the latest PR merged to that repo and notify the #incidents Slack channel."
The agent calls pagerduty_get_incidents → get_pull_requests → slack_send_message.
AI research
"Find the most downloaded text-generation models on HuggingFace, then search GitHub for projects using the top one."
The agent calls hf_search_models → search_repos.
Developer onboarding
"Who are the top 5 contributors to this repo? Get their GitHub profiles and create a Notion page summarizing the team."
The agent calls get_repo_contributors → get_user_profile (×5) → notion_create_page.
Setup
1. Clone and install
git clone https://github.com/Abdessamad-Y/mcp-github-agent.git
cd mcp-github-agent
pip install -r requirements.txt
2. Configure your services
cp .env.example .env
# Add tokens only for the services you want to use
# Unused services are safely ignored
3. Connect to your AI client
→ See docs/integrations.md for setup guides:
- Claude Desktop — native MCP, no code needed
- Claude API — Python script with agentic loop
- OpenAI (GPT-4o) — via
openai-agentsSDK - Azure OpenAI — via OpenAI SDK + Azure endpoint
- Cursor / VS Code Copilot
Run the demo
GITHUB_TOKEN=your_token python demo.py
Runs 4 live scenarios with formatted terminal output. No AI API key needed.
Run the examples
# With Claude API
GITHUB_TOKEN=your_token ANTHROPIC_API_KEY=your_key python examples/with_claude.py
# With OpenAI
GITHUB_TOKEN=your_token OPENAI_API_KEY=your_key python examples/with_openai.py
# With Azure OpenAI
GITHUB_TOKEN=your_token \
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com \
AZURE_OPENAI_KEY=your_key \
AZURE_OPENAI_DEPLOYMENT=gpt-4o \
python examples/with_azure_openai.py
Project structure
mcp-github-agent/
├── server.py # Entry point — registers all tools
├── mcp_instance.py # Shared FastMCP instance
├── tools/
│ ├── github.py # 11 GitHub tools
│ ├── jira.py # 4 Jira tools
│ ├── azure_devops.py # 4 Azure DevOps tools
│ ├── slack.py # 3 Slack tools
│ ├── pagerduty.py # 3 PagerDuty tools
│ ├── linear.py # 3 Linear tools
│ ├── notion.py # 3 Notion tools
│ ├── huggingface.py # 3 HuggingFace tools
│ └── weather.py # 2 OpenWeather tools
├── examples/
│ ├── with_claude.py
│ ├── with_openai.py
│ └── with_azure_openai.py
├── docs/
│ ├── how-it-works.md
│ ├── use-cases.md
│ └── integrations.md
├── demo.py
├── requirements.txt
└── .env.example
Stack
| Language | Python 3.10+ |
| MCP SDK | mcp[cli] — Anthropic's official Python SDK |
| HTTP client | httpx |
| Terminal output | rich |
Docs
| How it works | Architecture, MCP protocol, transport |
| Use cases | Real-world prompts and agent behavior |
| Integrations | Setup for Claude, OpenAI, Azure OpenAI, Cursor, VS Code |
License
MIT
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。