MCP Crew AI Server

@adam-paterson

リポジトリ

基本情報

カテゴリ

推論

トランスポート

stdio

作者

adam-paterson

MCP Crew AI Server について

MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows.

#ai #mcp #agents #mcp-server

MCP Crew AI Server の設定

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": {
    "mcp-crew-ai": {
      "command": "uvx",
      "args": [
        "mcp-crew-ai",
        "--agents",
        "path/to/agents.yml",
        "--tasks",
        "path/to/tasks.yml"
      ]
    }
  }
}

MCP Crew AI Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is MCP Crew AI Server?

MCP Crew AI Server is a lightweight Python server that runs, manages, and creates CrewAI multi-agent workflows. It uses the Model Context Protocol (MCP) to communicate with LLMs and tools like Claude Desktop or Cursor IDE, enabling orchestration of agentic tasks without custom code.

How to use MCP Crew AI Server?

Install via pip install mcp-crew-ai (or from GitHub or clone). Run with mcp-crew-ai --agents path/to/agents.yml --tasks path/to/tasks.yml or using uvx mcp-crew-ai with the same flags. Additional options include --topic, --process, --verbose, and --variables for YAML templating.

Key features of MCP Crew AI Server

Automatically loads agent/task configs from agents.yml and tasks.yml.
Supports custom YAML paths via --agents and --tasks command arguments.
Runs workflows through the MCP run_workflow tool.
Operates locally in STDIO mode for development and testing.
Allows variable substitution in YAML files via --variables.
Offers sequential or hierarchical process types.

Use cases of MCP Crew AI Server

Automating multi-step research and reporting tasks with AI agents.
Generating content (e.g., zoo updates) by assigning roles and goals.
Running templated workflows from IDEs like Cursor or from Claude Desktop.
Rapidly prototyping CrewAI workflows without writing Python code.

よくある質問

What is the runtime requirement?

Python 3.11+ along with MCP SDK, CrewAI, and PyYAML.

Can I use custom configuration files?

Yes, you specify paths via `--agents` and `--tasks` command line options.

How does variable substitution work?

Use `--variables` with a JSON string or file path; placeholders like `{topic}` in YAML are replaced at runtime.

Does it support different workflow processes?

Yes, you can choose `sequential` (default) or `hierarchical` with the `--process` flag.

Is there a default configuration if no files are given?

No; both `--agents` and `--tasks` are required when running the server with `mcp-crew-ai`. The `uvx mcp-crew-ai-server` command uses defaults from environment variables.

「推論」の他のコンテンツ

IntelliNode Medical Use Cases

Barqawiz

Multi-Agent AI Orchestration Workshop

MCP Sandbox

JohanLi233

Python sandboxes for llms

Agentic MCP Client

peakmojo

A standalone agent runner that executes tasks using MCP (Model Context Protocol) tools via Anthropic Claude, AWS BedRock and OpenAI APIs. It enables AI agents to run autonomously in cloud environments and interact with various systems securely.

Code Reasoning MCP Server

mettamatt

A code reasoning MCP server, a fork of sequential-thinking

255

n8n Workflow Builder MCP

ifmelate

MCP server that allow LLM in agent mode builds n8n workflows for you