MCP Crew AI Server: Effortless Automation & Seamless Scaling

MCP Crew AI Server: A Python-powered, lightweight powerhouse for seamlessly running, managing, and scaling CrewAI workflows—effortless control meets robust automation.

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30% of users reported increased productivity after just one week

About MCP Crew AI Server

What is MCP Crew AI Server: Effortless Automation & Seamless Scaling?

MCP Crew AI Server is a minimalist Python framework designed to streamline the orchestration of AI-driven workflows. Leveraging the Model Context Protocol (MCP), it enables teams to deploy Large Language Models (LLMs) and tools like Claude Desktop or Cursor IDE with minimal boilerplate coding. Think of it as the command center for automating complex tasks, abstracting away the heavy lifting of configuration and scaling.

Key Features of MCP Crew AI Server: Effortless Automation & Seamless Scaling?

Auto-Configurator: Instantly load agent/task blueprints from YAML files without writing setup code
CLI Powerhouse: Override default paths with command line flags for flexible deployments
Workflow Engine: Execute pre-designed processes via MCP's run_workflow tool
Dev-friendly Mode: Local STDIO operation for rapid iteration cycles

MCP Crew AI Server Features

How to Use MCP Crew AI Server: Effortless Automation & Seamless Scaling?

Start by installing via PyPI (pip install mcp-crew-ai). Define your agents and tasks in YAML files:

agents.yml:
    zookeeper:
      role: Wildlife Manager
      goal: Optimize zoo operations
      backstory: "Experienced in conservation strategies..."
  tasks.yml:
    daily_report:
      description: "Generate visitor engagement summary"
      output: report.md
      agent: zookeeper

Deploy with: mcp-crew-ai --agents config/agents.yml --tasks config/tasks.yml

Use Cases of MCP Crew AI Server: Effortless Automation & Seamless Scaling?

Content Farms: Automate article generation across multiple personas
Enterprise Workflows: Coordinate multi-agent teams for complex analysis
Rapid Prototyping: Test AI-driven processes in development environments
Dynamic Scaling: Adjust agent-task ratios based on workload spikes

MCP Crew AI Server FAQ

FAQ from MCP Crew AI Server: Effortless Automation & Seamless Scaling?

How do I customize variables in configurations?: Use --variables flag to inject JSON values: --variables '{"budget": 5000}'
Can I use this with Python 3.10?: No, requires Python 3.11+ for type hinting and concurrency features
What's the difference between sequential and hierarchical processing?: Sequential runs agents one-by-one; hierarchical allows parallel task execution
Where are logs stored by default?: In /var/log/mcp-crew-ai unless configured otherwise

Content

MCP Crew AI Server

MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows. This project leverages the Model Context Protocol (MCP) to communicate with Large Language Models (LLMs) and tools such as Claude Desktop or Cursor IDE, allowing you to orchestrate multi-agent workflows with ease.

Features

Automatic Configuration: Automatically loads agent and task configurations from two YAML files (agents.yml and tasks.yml), so you don't need to write custom code for basic setups.
Command Line Flexibility: Pass custom paths to your configuration files via command line arguments (--agents and --tasks).
Seamless Workflow Execution: Easily run pre-configured workflows through the MCP run_workflow tool.
Local Development: Run the server locally in STDIO mode, making it ideal for development and testing.

Installation

There are several ways to install the MCP Crew AI server:

Option 1: Install from PyPI (Recommended)

pip install mcp-crew-ai

Option 2: Install from GitHub

pip install git+https://github.com/adam-paterson/mcp-crew-ai.git

Option 3: Clone and Install

git clone https://github.com/adam-paterson/mcp-crew-ai.git
cd mcp-crew-ai
pip install -e .

Requirements

Python 3.11+
MCP SDK
CrewAI
PyYAML

Configuration

agents.yml: Define your agents with roles, goals, and backstories.
tasks.yml: Define tasks with descriptions, expected outputs, and assign them to agents.

Exampleagents.yml:

zookeeper:
  role: Zookeeper
  goal: Manage zoo operations
  backstory: >
    You are a seasoned zookeeper with a passion for wildlife conservation...

Exampletasks.yml:

write_stories:
  description: >
    Write an engaging zoo update capturing the day's highlights.
  expected_output: 5 engaging stories
  agent: zookeeper
  output_file: zoo_report.md

Usage

Once installed, you can run the MCP CrewAI server using either of these methods:

Standard Python Command

mcp-crew-ai --agents path/to/agents.yml --tasks path/to/tasks.yml

Using UV Execution (uvx)

For a more streamlined experience, you can use the UV execution command:

uvx mcp-crew-ai --agents path/to/agents.yml --tasks path/to/tasks.yml

Or run just the server directly:

uvx mcp-crew-ai-server

This will start the server using default configuration from environment variables.

Command Line Options

--agents: Path to the agents YAML file (required)
--tasks: Path to the tasks YAML file (required)
--topic: The main topic for the crew to work on (default: "Artificial Intelligence")
--process: Process type to use (choices: "sequential" or "hierarchical", default: "sequential")
--verbose: Enable verbose output
--variables: JSON string or path to JSON file with additional variables to replace in YAML files
--version: Show version information and exit

Advanced Usage

You can also provide additional variables to be used in your YAML templates:

mcp-crew-ai --agents examples/agents.yml --tasks examples/tasks.yml --topic "Machine Learning" --variables '{"year": 2025, "focus": "deep learning"}'

These variables will replace placeholders in your YAML files. For example, {topic} will be replaced with "Machine Learning" and {year} with "2025".

Contributing

Contributions are welcome! Please open issues or submit pull requests with improvements, bug fixes, or new features.

Licence

This project is licensed under the MIT Licence. See the LICENSE file for details.

Happy workflow orchestration!