Observe and operate Hatchet workflows from an AI agent — runs, logs, trigger, cancel, replay.
Hatchet MCP server is a hosted integration for AI assistants that speak the Model Context Protocol. Observe and operate Hatchet workflows from an AI agent — runs, logs, trigger, cancel, replay.
An MCP server that lets AI agents observe and operate Hatchet workflows — status, runs, logs, workers, and metrics, plus trigger / cancel / replay.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
You will need 2 environment variables: HATCHET_CLIENT_TOKEN, HATCHET_TENANT_ID. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
This sits in the monitoring and observability group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by ElliotPadfield; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Hatchet's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"hatchet": {
"command": "npx",
"args": ["-y", "hatchet-mcp"],
"env": { "HATCHET_CLIENT_TOKEN": "<your-hatchet-api-token>" }
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| HATCHET_CLIENT_TOKEN | Credential the server authenticates with. | Yes |
| HATCHET_TENANT_ID | Configuration value read at startup. | Optional |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.