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mcp-prefect-orchestrator

Facilitates natural language interaction with the Prefect workflow orchestration platform for managing pipelines, scheduling definitions, and atomic operations. Enables streamlined environmental oversight and control to enhance the efficacy of automated workflows.

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mcp-prefect-orchestrator logo

allen-munsch

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GitHub GitHub Stars 15
NPM Weekly Downloads 79
Tools 1
Last Updated 2026-02-19

Tags

workflowtoolsmcpmcp prefectworkflow managementinteract prefect

Prefect Orchestration Interface (MCP Server)

This implementation of the Model Context Protocol (MCP) server is engineered to provide seamless, vernacular interaction with the Prefect system, enabling intelligent agents to issue commands regarding workflow assets.

Core Capabilities

This server exposes control over the following Prefect subsystems via abstraction layers:

  • Workflow Blueprint Oversight: Retrieval, inspection, and decommissioning of defined workflows (Flows).
  • Workflow Instance Control: Initiation, status monitoring, and termination/resumption of execution instances (Flow Runs).
  • Schedule Definition Management: Configuration and maintenance of deployment schedules.
  • Execution Step Tracking: Monitoring and governance of individual operational units (Task Runs).
  • Workload Distribution Queues: Administration of intake channels for pending tasks (Work Queues).
  • Configuration Artifact Access: Interfacing with stored credentials and settings (Blocks).
  • Global Parameterization: Management of environment-wide configurable values (Variables).
  • Contextual Environment Details: Querying organizational scope metadata (Workspaces).

Setup Prerequisites

The operational environment requires setting the following environment parameters:

bash export PREFECT_API_URL="http://localhost:4200/api" # Endpoint address for the Prefect API service export PREFECT_API_KEY="your_api_key" # Authentication token, mandatory for cloud deployments

Deployment Procedure

To launch the MCP interface service:

bash docker compose up

Illustrative Conversational Inputs

Once the connection is established, assistants can interpret user intent mapped to Prefect operations. Examples include:

  • "Display every registered workflow."
  • "Enumerate all flow executions that failed within the last twenty-four hours."
  • "Initiate the run sequence associated with the 'data-processing' deployment specification."
  • "Temporarily suspend the ongoing temporal execution plan for the 'daily-reporting' artifact."
  • "Report the final state of the most recent ETL flow instance."

Development Roadmap & Extension Guide

Certain backend functionalities remain under active development.

Integrating Novel Operations

To introduce a new capability within an existing API domain:

  1. Implement the requisite logic within the corresponding file located in src/mcp_prefect.
  2. Register the newly created function within the aggregate list returned by get_all_functions() in that module.

To onboard an entirely new operational domain (API type):

  1. Define the new domain constant in APIType within enums.py.
  2. Establish a dedicated module within the src/prefect/ directory structure.
  3. Update the bootstrap logic in main.py to incorporate the new API domain enumeration.

Execution Manifest Example

This configuration snippet demonstrates how the orchestrator service might be invoked within a larger system context:

{ "mcpServers": { "mcp-prefect-orchestrator": { "command": "mcp-prefect", "args": [ "--transport", "sse" ], "env": { "PYTHONPATH": "/path/to/your/project/directory" }, "cwd": "/path/to/your/project/directory" } } }

See Also

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