hatchet-mcp

Unofficial, community project — not affiliated with, sponsored by, or endorsed by Hatchet. It is an independent integration that talks to Hatchet only through the public REST API via the official hatchet-sdk.

An operational MCP (Model Context Protocol) server for Hatchet, the durable orchestration engine. It exposes Hatchet's live state — and, optionally, its control surface — to an LLM over the Hatchet REST API (via the official hatchet-sdk feature clients) using the stdio transport.

It is a control tower for a Hatchet tenant: inspect workflow definitions, runs, tasks, logs, workers, events, cron/scheduled triggers, rate limits, filters, queue/task metrics, run timings, and OpenTelemetry traces — and, when explicitly enabled, trigger/cancel/replay runs, push events, pause/resume workflows and workers, and manage cron/scheduled/filters.

Read-only by default. Out of the box it never triggers, cancels, replays, or mutates anything, so it is safe to point at a production tenant. Mutating tools are unregistered until you opt in with HATCHET_MCP_READ_ONLY=false.

Design background lives in docs/init/. See docs/init/mcp-server-design.md for the full tool surface and security model.

Quick start

1. Get a token. Visit your Hatchet dashboard → Settings → API Tokens → Create. The token is shown only once. (For self-hosted with an internal token URL, note the public REST URL — set it as HATCHET_CLIENT_SERVER_URL.)
2. Run the server. uvx hatchet-mcp (works with no prior install beyond uv itself).
3. Set the token. Either export HATCHET_CLIENT_TOKEN=<your-token> in your shell, or put it in your MCP client's env: block — see Use it from an MCP client for a copy-pasteable .mcp.json.
4. Ask the LLM. Try "list my workflows" or "show runs from the last hour". The 24 read tools are available immediately; mutating tools stay hidden until you set HATCHET_MCP_READ_ONLY=false.

Install & run

# From PyPI
uvx hatchet-mcp

# Directly from a git checkout, no install
uvx --from git+https://github.com/DanMeon/hatchet-mcp hatchet-mcp

# From a local clone
uvx --from . hatchet-mcp

# Local development
uv run hatchet-mcp

All launch the same stdio MCP server. Configuration is environment-only because uvx makes passing CLI flags awkward. With no HATCHET_CLIENT_TOKEN the server fails fast and exits non-zero — it never starts a half-configured server.

Use it from an MCP client

The repo ships a .mcp.json.example for project-scoped setup. Copy it to .mcp.json (gitignored, so your token stays local) and fill in the token:

cp .mcp.json.example .mcp.json
# then edit .mcp.json → set HATCHET_CLIENT_TOKEN

Or register it directly with the CLI:

claude mcp add hatchet -e HATCHET_CLIENT_TOKEN=<your-token> -- uvx hatchet-mcp

Or the JSON config block (Claude Desktop / Claude Code). This example opts into the mutating tools:

{
  "mcpServers": {
    "hatchet": {
      "command": "uvx",
      "args": ["hatchet-mcp"],
      "env": {
        "HATCHET_CLIENT_TOKEN": "<your-token>",
        "HATCHET_CLIENT_SERVER_URL": "https://<self-host-url>",
        "HATCHET_MCP_READ_ONLY": "false"
      }
    }
  }
}

Omit HATCHET_MCP_READ_ONLY (or set it to true) to keep the safe, read-only posture.

Multiple tenants

A Hatchet token is scoped to exactly one tenant, so to operate several tenants you run one server instance per tenant — each with its own token — and register them as distinct MCP servers. No special configuration is needed; the client picks the server by name.

{
  "mcpServers": {
    "hatchet-prod": {
      "command": "uvx",
      "args": ["hatchet-mcp"],
      "env": { "HATCHET_CLIENT_TOKEN": "<prod-token>" }
    },
    "hatchet-staging": {
      "command": "uvx",
      "args": ["hatchet-mcp"],
      "env": { "HATCHET_CLIENT_TOKEN": "<staging-token>", "HATCHET_MCP_READ_ONLY": "false" }
    }
  }
}

This keeps tenant tokens in separate processes (no token mixing). See docs/init/multitenancy-and-dependencies.md §1 for the rationale and the alternative (a single server with a tenant argument), which was considered and not adopted.

Configuration (environment only)

Env var	Required	Default	Meaning
HATCHET_CLIENT_TOKEN	yes	—	Hatchet API token (a JWT). Server URL and tenant are decoded from it.
HATCHET_CLIENT_SERVER_URL	no	from token	REST base URL override (for a self-host whose token embeds an unreachable internal URL).
HATCHET_MCP_READ_ONLY	no	true	The mutation gate. true registers only the 24 read tools; false additionally registers the 17 mutating tools. Accepts true/false/1/0/yes/no/on/off; an unrecognized value fails fast.

If HATCHET_CLIENT_TOKEN is missing the server fails fast at startup and exits — no silent fallback. The token is never logged, echoed, or included in any error message (even when the SDK rejects it, only the exception type is surfaced).

Safety model

This server can drive a production orchestrator, so safety is layered:

• Read-only by default. HATCHET_MCP_READ_ONLY defaults to true. In that mode the 17 mutating tools are not registered at all — an MCP client cannot see or call them.
• Defense in depth. Even if a mutating handler is somehow reached in read-only mode, it re-checks the gate and refuses.
• Destructive annotations. Every mutating tool is advertised with destructiveHint=true (and an accurate idempotentHint) so MCP clients can require human approval before each call.
• Bulk guardrails. cancel_runs / replay_runs default to a dry run — they return the matching run IDs without acting. You must re-call with dry_run=false to mutate. They also refuse to act on more than 500 matching runs, forcing a narrower filter.
• Token confidentiality. The token is never written to stdout (the JSON-RPC channel), stderr, tool responses, or error messages.
• Single tenant. One token scopes the whole server to one Hatchet tenant (its sub claim).

Tools

41 tools total: 24 read-only (always registered) + 17 mutating (registered only when HATCHET_MCP_READ_ONLY=false).

Read-only (24) — always available

Tool	Hatchet SDK / REST call	Purpose
list_workflows	h.workflows.aio_list	List workflow definitions (name, paused, versions)
get_workflow	h.workflows.aio_get	One workflow definition by ID
list_runs	h.runs.aio_list	List workflow/task runs (time, status, workflow, worker, metadata filters). Excludes payloads by default — use get_run for inputs/outputs
get_run	h.runs.aio_get	One run in detail (task tree / DAG shape, inputs, outputs)
get_run_status	h.runs.aio_get_status	Status only — lightweight polling
get_task	h.runs.aio_get_task_run	One task run by ID (status, attempt, worker, I/O)
get_task_logs	h.logs.aio_list	Log lines for a single task run
list_task_events	TaskApi.v1_task_event_list	Orchestration event timeline for a task run (state transitions)
list_workers	h.workers.aio_list	List workers (status, slots, registered actions)
get_worker	h.workers.aio_get	One worker by ID (slot state, recent runs, registered actions)
list_events	EventApi.v1_event_list	List ingested events (key, time, workflow, run-status, metadata)
get_event	EventApi.v1_event_get	One event with full payload + triggered-run summary
list_event_keys	EventApi.v1_event_key_list	Distinct event keys in the tenant
list_crons	h.cron.aio_list	List cron triggers
get_cron	h.cron.aio_get	One cron trigger by ID
list_scheduled	h.scheduled.aio_list	List scheduled (one-off, future) runs
list_rate_limits	h.rate_limits.aio_list	Rate limits with current consumption
list_filters	h.filters.aio_list	Event filters (CEL expressions)
get_filter	h.filters.aio_get	One event filter by ID
cel_debug	h.cel.aio_debug	Test a CEL expression against sample input (no filter created)
get_queue_metrics	h.metrics.aio_get_queue_metrics	Native per-queue depth
get_task_metrics	h.metrics.aio_get_task_metrics	Task counts grouped by status
get_run_timings	WorkflowRunsApi.v1_workflow_run_get_timings	Task waterfall timings for a run
get_trace	ObservabilityApi.v1_observability_get_trace	OpenTelemetry spans for a run

Mutating (17) — only when HATCHET_MCP_READ_ONLY=false

Tool	Hatchet SDK / REST call	Idempotent	Purpose
trigger_workflow	h.runs.aio_create	no	Trigger a new workflow run by name
cancel_runs	h.runs.aio_bulk_cancel	no	Cancel runs by IDs or filter — dry-run default, 500 cap
replay_runs	h.runs.aio_bulk_replay	no	Replay runs by IDs or filter — dry-run default, 500 cap
restore_task	TaskApi.v1_task_restore	no	Restore an evicted durable task
push_event	EventApi.event_create	no	Push an event (may trigger event-driven workflows)
pause_workflow	WorkflowApi.workflow_update (isPaused=true)	yes	Pause a workflow definition
resume_workflow	WorkflowApi.workflow_update (isPaused=false)	yes	Resume a paused workflow definition
pause_worker	h.workers.aio_pause	yes	Pause a worker
resume_worker	h.workers.aio_unpause	yes	Resume a worker
create_cron	h.cron.aio_create	no	Create a cron trigger
delete_cron	h.cron.aio_delete	yes	Delete a cron trigger
create_scheduled	h.scheduled.aio_create	no	Schedule a one-off future run
delete_scheduled	h.scheduled.aio_delete	yes	Delete a scheduled run
reschedule	h.scheduled.aio_update	yes	Change a scheduled run's trigger time
create_filter	h.filters.aio_create	no	Create an event filter (CEL)
update_filter	h.filters.aio_update	yes	Update an event filter
delete_filter	h.filters.aio_delete	yes	Delete an event filter

Status enums differ by engine generation and are passed through unchanged (no lossy remapping). v1 runs/tasks use QUEUED · RUNNING · COMPLETED · CANCELLED · FAILED; legacy objects use PENDING · RUNNING · SUCCEEDED · FAILED · CANCELLED · QUEUED · BACKOFF. See docs/init/overview-and-concepts.md §5.

Response size and limits

A live tenant can hold thousands of runs, each with large input/output payloads, so a naive list_runs can return several megabytes and blow past an MCP client's context window. The read tools follow the same "keep lists small, fetch detail on demand" pattern as the GitHub, Sentry, and Grafana MCP servers:

• Lists exclude heavy payloads by default. list_runs returns run metadata — id, status, workflow, timings — but not each run's input/output. Pass include_payloads=true (ideally with a small limit) for the full rows, or fetch one run's payloads with get_run.
• Every list tool defaults and caps its limit. Unset resolves to 50, the hard cap is 100, and a value below 1 is rejected. get_task_logs is the exception — 1000 log lines, capped at 1000. Use offset to page where the tool exposes it.
• Single-item get_* tools are never truncated. get_run / get_task / get_event return the whole object — the one item is the point, so they bypass the size guard below.
• A ~500 KB ceiling backstops every list result. Anything larger is rejected with a message explaining how to narrow it (smaller limit, tighter time window or statuses) instead of silently overflowing your context. This also protects tools whose upstream call takes no limit, such as list_workers.

Resources & prompts

In addition to tools, the server exposes read-only MCP resources (URI-addressable views that reuse the read tools, so their JSON is identical) and operator prompts. Both are always available and add no mutating surface.

Resources:

URI	Backed by
hatchet://workflows	list_workflows
hatchet://workflows/{workflow_id}	get_workflow
hatchet://workers	list_workers
hatchet://runs/{workflow_run_id}	get_run
hatchet://runs/{workflow_run_id}/status	get_run_status

Prompts:

Prompt	Arguments	Orchestrates
triage_failed_runs	hours (default 24)	list_runs → get_run → get_task_logs
debug_run	workflow_run_id	get_run → get_run_timings → get_task_logs → get_trace
tenant_health	hours (default 24)	get_queue_metrics → get_task_metrics → list_workers

How it talks to Hatchet

MCP client (Claude Code, …)
   │  stdio (JSON-RPC)
   ▼
hatchet-mcp (Python, FastMCP)
   │  hatchet-sdk feature clients, async aio_* methods
   │  Authorization: Bearer <HATCHET_CLIENT_TOKEN>
   ▼
Hatchet REST API   (/api/v1/stable/… + /api/v1/…)

REST only — never the gRPC worker dispatcher protocol. A single token scopes the server to a single tenant (resolved from the token's sub claim by the SDK).

Development

uv sync                 # install runtime + dev deps on Python 3.10+
uv run pytest           # full test suite — needs no token, performs no real mutation
uv run ruff check src tests
uv run ruff format --check src tests
uv run pyright
uv run hatchet-mcp      # needs HATCHET_CLIENT_TOKEN in the environment

Contributing

Contributions are welcome. See CONTRIBUTING.md for development setup, quality gates, project conventions, and the maintainer release process.

Security

hatchet-mcp can drive a production orchestrator and handles an API token. Please report vulnerabilities privately — see SECURITY.md. Don't open a public issue for security reports.

License

MIT — see LICENSE.

"Hatchet" and any related marks belong to their respective owners. This is an independent project and is not affiliated with or endorsed by Hatchet.