Near-Intents MCP Agentkit Server

Provides an MCP server for AI agents and task orchestration using the CrewAI framework.

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6 months ago

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2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "matthewlaw1-near-intents-mcp-agentkit": {
      "command": "python3",
      "args": [
        "src/crew_server.py"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

This MCP server enables AI agent creation, task management, and coordinated workflows using the CrewAI framework. It lets you define agents, assign tasks, and run crews to automate complex AI-driven processes, all through a configurable, local or remote server.

How to use

You interact with the Crew AI MCP server through a client that can send tool requests and orchestrate agents and tasks. Start by creating an agent that defines a role and goal, then generate a task for that agent, and finally assemble a crew to execute the task chain. You can run the crew to see the end-to-end workflow in action, with verbose output to understand each step of the process.

How to install

Prerequisites you need before installation include Python 3.8 or higher, the jq command-line tool for the setup workflow, and a suitable shell environment.

Follow these concrete steps to install and prepare the MCP server:

git clone <your-fork-or-clone-method>

# or use your preferred method to obtain the project

cd <repository-directory>

# Run the setup script to install dependencies and configure MCP settings
./crew.sh

Additional configuration and notes

Configure your OpenAI API key in the environment before running the server. The key provides access to OpenAI services used by agents and tasks.

export OPENAI_API_KEY="your-api-key"

Available tools

create_agent

Create a new AI agent with a specified role, goal, and backstory to guide its behavior.

create_task

Create a task describing the work to be performed by a specified agent, including expected outputs.

create_crew

Assemble agents and tasks into a crew and run the workflow with optional verbose output to monitor progress.