MCP-Agg: Multi-Channel Platform Aggregator

Name: MCP-Agg: Multi-Channel Platform Aggregator
Author: moosh3

@moosh3

代码仓库

关于 MCP-Agg: Multi-Channel Platform Aggregator

Aggregate MCP server

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

moosh3

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-agg": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP-Agg?

MCP-Agg is a unified API service that provides consistent access to multiple tools and platforms, including GitHub and Slack, through a single interface. It is designed for developers and teams seeking workflow automation and enhanced productivity by integrating various services.

How to use MCP-Agg?

Install Python 3.12+, PostgreSQL, and the uv package manager. Clone the repository, create a virtual environment, install dependencies, copy .env.example to .env, and run alembic upgrade head. Start the service with uvicorn api.main:app --reload --port 8000 (development) or docker-compose up -d (Docker). Register and log in, connect your accounts for each supported platform, generate an MCP URL from the endpoint, and use it in your MCP client configuration.

Key features of MCP-Agg

Unified tool interface across multiple platforms
Secure authentication and authorization per service
Extensible architecture to add new tools
MCP client support with unique generated URLs
Comprehensive documentation via Swagger UI and ReDoc

Use cases of MCP-Agg

Manage GitHub repositories, issues, and pull requests from one API
Post messages, reply to threads, and add reactions in Slack channels
Automate cross-platform workflows by combining GitHub and Slack actions
Access channel history and user profiles from Slack via a single endpoint
Integrate multiple services into an MCP client for centralized tool access

FAQ from MCP-Agg

What platforms does MCP-Agg support?

It currently supports GitHub (list repos, manage issues/PRs, access user profiles) and Slack (list channels, post messages, reply to threads, add reactions, access channel history, retrieve user profiles).

What are the runtime requirements?

Python 3.12 or higher, a PostgreSQL database, and the uv package manager are required.

How do I run MCP-Agg in production?

Start the application with uvicorn api.main:app --host 0.0.0.0 --port 8000 or use docker-compose up -d for a containerized setup.

How can I test MCP-Agg?

Run tests using python -m pytest and generate coverage reports with python -m pytest --cov=api.

How do I connect an MCP client?

Register and log in, connect your platform accounts (GitHub, Slack, etc.), navigate to the MCP URL generator endpoint, and use the generated URL in your MCP client configuration.

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