About CrewAI Enterprise MCP Server
CrewAI Enterprise MCP Server is an MCP server for starting deployed CrewAI workflows and checking their status and results. It suits users who have crews deployed on CrewAI and want to call them from an MCP client.
Key features
- 01Start a deployed crew
- 02Check crew run status and results
- 03Configure server URL and bearer token via environment variables
Use cases
- 01Triggering a deployed CrewAI workflow from Claude Desktop
- 02Checking progress and output of a crew run
MCP support
The server exposes kickoff_crew and get_crew_status as MCP tools for controlling deployed crews.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMECrewAI Enterprise MCP Server
Overview
A Model Context Protocol (MCP) server implementation that provides deployed CrewAI workflows. This server enables kicking off your deployed crew and inspect the status giving the results of your crew.
Tools
- kickoff_crew
- get_crew_status
Env Variables
retrieve from app.crewai.com
MCP_CREWAI_ENTERPRISE_SERVER_URL
MCP_CREWAI_ENTERPRISE_BEARER_TOKEN
Usage with Claude Desktop
To use this MCP server with Claude Desktop, follow these steps:
- Open Claude Desktop
- Go to Settings > Developer Settings
- Add a new MCP server with the configuration shown below
Locally, cloned repo:
Install mcp and mcp[cli] locally
{
"mcpServers": {
"crewai_enterprise_server": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"<filepath of cloned repo>",
"/crewai_enterprise_server.py"
],
"env": {
"MCP_CREWAI_ENTERPRISE_SERVER_URL": "<>",
"MCP_CREWAI_ENTERPRISE_BEARER_TOKEN": "<>"
}
}
}
}
TODO: Added on PyPI:
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.