What is Near Intents Mcp Agentkit

Near-Intents-MCP-Agentkit is an MCP server that leverages the CrewAI framework to provide AI agent and task management capabilities, allowing users to create agents, assign tasks, and manage workflows effectively.

Use cases

Use cases include creating a research agent to analyze market trends, managing multiple tasks across different agents for comprehensive project execution, and automating workflows to enhance productivity.

How to use

To use Near-Intents-MCP-Agentkit, clone or fork the repository, run the setup script to install dependencies and configure settings, and set your OpenAI API key. You can then create agents, tasks, and crews using JSON formatted requests.

Key features

Key features include the ability to create AI agents with specific roles and goals, assign tasks to these agents, and manage crews that can execute multiple tasks in a workflow, all through a simple API interface.

Where to use

Near-Intents-MCP-Agentkit can be used in various fields such as research, project management, and any domain requiring task automation and AI-driven analysis.

Crew AI MCP Server

An MCP server that provides AI agent and task management capabilities using the CrewAI framework.

Setup

1. Clone or fork this repository
2. Run the setup script:

./crew.sh

The setup script will:

• Install required Python dependencies
• Configure the MCP settings file for your system
• Set up the correct paths automatically

Configuration

Before using the server, set your OpenAI API key:

export OPENAI_API_KEY="your-api-key"

Usage

The server provides three main tools:

1. Create an Agent

{
  "method": "call_tool",
  "params": {
    "name": "create_agent",
    "arguments": {
      "role": "researcher",
      "goal": "Research and analyze information effectively",
      "backstory": "An experienced research analyst"
    }
  }
}

2. Create a Task

{
  "method": "call_tool",
  "params": {
    "name": "create_task",
    "arguments": {
      "description": "Analyze recent market trends",
      "agent": "researcher",
      "expected_output": "A detailed analysis report"
    }
  }
}

3. Create and Run a Crew

{
  "method": "call_tool",
  "params": {
    "name": "create_crew",
    "arguments": {
      "agents": [
        "researcher"
      ],
      "tasks": [
        "Analyze recent market trends"
      ],
      "verbose": true
    }
  }
}

Example Usage

Create and run a complete workflow:

(echo '{"method": "call_tool", "params": {"name": "create_agent", "arguments": {"role": "researcher", "goal": "Research and analyze information effectively", "backstory": "An experienced research analyst"}}}'; echo '{"method": "call_tool", "params": {"name": "create_task", "arguments": {"description": "Analyze recent market trends", "agent": "researcher", "expected_output": "A detailed analysis report"}}}'; echo '{"method": "call_tool", "params": {"name": "create_crew", "arguments": {"agents": ["researcher"], "tasks": ["Analyze recent market trends"], "verbose": true}}}') | python3 src/crew_server.py

System Requirements

• Python 3.8 or higher
• jq command-line tool (for setup script)
• VSCode with Roo Cline extension installed

Supported Platforms

• macOS
• Linux
• Windows (via Git Bash)

Troubleshooting

If you encounter any issues:

1. Ensure your OpenAI API key is set correctly
2. Check that all dependencies are installed (pip install -r requirements.txt)
3. Verify the MCP settings file exists and has the correct configuration
4. Make sure the server path in the MCP settings matches your actual file location

Contributing

1. Fork the repository
2. Create your feature branch
3. Make your changes
4. Run the setup script to verify everything works
5. Submit a pull request