MCP For Claude Marketplace
Prefect

Prefect MCP Connector for Claude

A+

Bring your data orchestration into your AI — audit Python pipelines, debug failed runs, and inspect Prefect Work Pools natively.

7 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Equip any AI agent with direct line-of-sight into your Prefect Cloud workspaces. Empower your LLMs to parse Python data pipelines, identify exactly why an ETL flow crashed, and audit underlying cloud infrastructure blocks conversational.

What you can do

  • Audit Pipelines & Runs — Ask the AI to fetch all list_flows and dissect their historical execution via list_flow_runs, identifying bottlenecks
  • Execution Breakdown — Command the agent to pull absolute tracing of a crashed workflow via get_flow_run to literally read the Python traceback
  • Infrastructure & Blocks — Let the agent audit secure list_blocks connections (AWS, GCP) binding your Prefect environments
  • Automations & Triggers — Instantly review list_automations dictating active webhook-based flow triggers

How it works

  1. Subscribe to this MCP server
  2. Provide your Prefect API Key, Account ID, and Workspace ID
  3. Engage with your flows natively from Cursor, Claude, or any compatible client

Stop digging through logs across scattered pipelines. When a data sync fails, ask 'Why did the Nightly Stripe Sync fail?' and watch the AI extract the explicit HTTP/Python errors directly from Prefect.

Who is this for?

  • Data Engineers — troubleshoot complex DAGs parsing exact step-by-step metadata without leaving your IDE
  • Data Scientists — verify if your ML model retraining succeeded on your remote compute clusters
  • DevOps Ops — audit routing behaviors exploring list_work_pools pushing jobs to remote Docker and Kubernetes instances
data-orchestrationworkflow-automationpipeline-monitoringpython-automationetl-managementtask-scheduling

7 tools expose this connector's capabilities to your AI agent.

list_flows

List all engineered Python workflows registered natively on Prefect Cloud

list_deployments

List all active deployments representing scheduled or triggered physical workflow instances

list_flow_runs

List recent active, scheduled, or failed flow runs recording actual physical data pipelining limits

get_flow_run

Get complete contextual metadata, runtime limits, and specific variables tied to an executed Prefect Flow Run

list_work_pools

List all physical Work Pools acting as routing destinations for dynamically dispatched flow runs

list_blocks

List all secure infrastructure Blocks defining Secrets, AWS paths, or GCP configurations directly in Prefect

list_automations

List all Cloud Automations mapping explicit webhook/event actions dictating real-time flow triggers

See how to talk to your AI agent using Prefect.

Did the 'DB Sync Hourly' flow experience any failed runs today? Provide the traceback.

Accessing `list_flow_runs`, I see a FAILED state roughly 2 hours ago. Pulling metadata via `get_flow_run` under ID `f391...`, the worker returned `psycopg2.OperationalError: server closed the connection unexpectedly`. Looks like a database timeout.

Show me what infrastructure is tied to our 'Production Data Warehouse' deployment.

Looking at `list_deployments` and `list_work_pools`, the 'Production Data Warehouse' deployment routes exclusively to the Work Pool named `eks-primary-cluster`. This compute layer strictly consumes infrastructure bounds listed in your `AWS-ECS-Credentials` Block.

List all active automations tracking webhook payloads.

Running `list_automations` globally. I discovered 2 explicit rules: 'Slack Incident Notifier' reacting to Flow FAILED triggers, and 'K8s Pod Cleanup' executing automatically when an ECS cluster returns 'Destroyed' payloads.

Yes. Upon discovering a FAILED execution with `list_flow_runs`, it uses `get_flow_run` to unpack the explicit metadata and stack trace, isolating exactly what task and line broke your ETL logic.

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