pulsemcp Safe content atomic mcp-remote

Prefect

This MCP server provides AI assistants with complete Prefect workflow orchestration capabilities, built using Python with FastMCP and the Prefect client SDK to enable natural language control over flow management, deployment operations, task monitoring, and infrastructure management. The implementation offers full CRUD operations across flows, flow runs, deployments, task runs, work queues, variables, and blocks, with UI URL generation for seamless web interface integration and comprehensive filtering capabilities for workflow discovery and monitoring. Built with Docker containerization, SSE transport support, environment-based configuration, and extensive test coverage using pytest with real Prefect server integration, it serves data engineering teams needing conversational access to workflow orchestration, DevOps engineers requiring AI-driven deployment management, and operations teams where natural language interfaces enhance Prefect administration without direct API knowledge.

Cognium trust score

50%

Tier

Unverified

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

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Metadata

Version: 1.0.0
Skill type: atomic
Execution layer: mcp-remote
Category: cloud-infra
Source: PulseMCP
Repository: github.com/allen-munsch/mcp-prefect
Author type: human
Updated: 2026-06-16

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MCP

Resolve Prefect 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"