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CrewAI Workflow

A lightweight Python server for running CrewAI multi-agent workflows through the Model Context Protocol. Designed to automatically load agent and task configurations from YAML files, enabling flexible AI task orchestration with minimal custom code. Supports dynamic agent and task creation, variable templating, and seamless integration with tools like Claude Desktop or Cursor IDE.

Cognium trust score

89%

Tier

Verified

Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.

Scan details: Circle-IR · 2026-09-19 · Appeal

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Metadata

Version: 1.0.0
Skill type: atomic
Execution layer: mcp-remote
Category: productivity
Source: PulseMCP
Repository: github.com/adam-paterson/mcp-crew-ai
Author type: human
Last scanned: 2026-09-19
Updated: 2026-09-19

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MCP

Resolve CrewAI Workflow from your agent

Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.

One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.

claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp

Swap --scope user for --scope project to commit it to .mcp.json.

Your client Same endpoint everywhere; only the config key differs. The full list is in the descriptor.

Claude Desktop · Settings → Connectors → Add custom connector

https://api.skillsregistry.net/mcp

Paste the URL as the connector endpoint. No authentication.

ChatGPT · Settings → Connectors → Create (developer mode) → MCP server URL

https://api.skillsregistry.net/mcp

Authentication: none. Works in chat and deep research — the server exposes the connector search and fetch tools.

Codex CLI · ~/.codex/config.toml

[mcp_servers.skillsregistry]
url = "https://api.skillsregistry.net/mcp"