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Slack MCP Host

@jexp

リポジトリ

Slack MCP Host について

MCP Host Server Application for Slack

#slack#mcp#bot#integration

基本情報

カテゴリ

コミュニケーション

ライセンス

MIT license

ランタイム

python

トランスポート

stdio

公開者

jexp

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Slack MCP Host?

A Slack bot that integrates MCP (Machine Control Protocol) servers with Slack, providing LLM-powered interactions in dedicated channels. It can execute tools from configured MCP servers and present results in a user-friendly format.

How to use Slack MCP Host?

Install dependencies, configure environment variables (Slack token, OpenAI key, MCP server definitions), and deploy to Cloudflare Workers or run locally with uvicorn. Interact with the bot by mentioning it or messaging in designated Slack channels.

Key features of Slack MCP Host

  • LLM-powered conversations in Slack
  • Integration with multiple MCP servers
  • Markdown rendering support
  • Collapsible tool execution results
  • Conversation history maintenance
  • Secure configuration management

Use cases of Slack MCP Host

  • Query a Neo4j movie database for film details
  • Retrieve current time or date via MCP time server
  • Execute any tool from configured MCP servers in Slack
  • Enable team collaboration with an LLM-powered Slack bot

FAQ from Slack MCP Host

What prerequisites are needed to run Slack MCP Host?

Python 3.11+, the uv package manager, a Slack workspace with admin access, an OpenAI API key, and one or more MCP servers.

How do I configure MCP servers?

Define each MCP server as an environment variable with a prefix like SLACK_MCP_ (e.g., SLACK_MCP_NEO4J_MOVIES=uvx mcp-neo4j-cypher --url ...).

How do I deploy Slack MCP Host?

You can deploy to Cloudflare Workers using Wrangler, or run locally with uvicorn src.api:app --reload --port 8000 and expose via ngrok.

What LLM model does the bot use?

By default it uses GPT-4, as configured in the .env file with OPENAI_MODEL=gpt-4.

Does the bot support formatting and collapsible results?

Yes, it renders Markdown and shows tool execution results inside collapsible <details> elements.

コメント

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