CrewAI (Near Intents)

by matthewlaw1

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GitHub

About

Leverages the CrewAI framework in combination with OpenAI API to orchestrate multi-agent workflows for automated research, data analysis, and problem-solving across domains.

Details

Author: matthewlaw1
Repository: MatthewLaw1/Near-Intents-MCP-Agentkit
GitHub stars: 3
License: Other
Categories: Productivity, Developer Tools, Design, Workplace, AI, Search, Project Management, API, Infrastructure, Communication

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name CrewAI (Near Intents)

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

From the repository

The server provides three main tools:

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

`create_agent`

Create an agent with specified role, goal, and backstory. Parameters: role (string), goal (string), backstory (string)

`create_task`

Create a task for a specified agent with a description and expected output. Parameters: description (string), agent (string), expected_output (string)

`create_crew`

Create and run a crew with specified agents and tasks. Parameters: agents (array of strings), tasks (array of strings), verbose (boolean)

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "crewai (near intents)": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Macos

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "npx",
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "cmd"
}

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

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Details

Jump to

Setting up with Highlight

From the repository

create_agent

create_task

create_crew

Claude Desktop / Cursor

Linux

Macos

Windows

Crew AI MCP Server

Setup

Configuration

Usage

1. Create an Agent

2. Create a Task

3. Create and Run a Crew

Example Usage

System Requirements

Supported Platforms

Troubleshooting

Contributing

Install

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`create_agent`

`create_task`

`create_crew`