Temporal MCP Server

Overview

This is a Model Context Protocol (MCP) server that provides tools for interacting with Temporal workflow orchestration. It enables AI assistants and other MCP clients to manage Temporal workflows, schedules, and workflow executions through a standardized interface. The server supports both local and remote Temporal instances.

Read more on the Temporal Code Exchange

Distributions

• PyPI - https://pypi.org/project/temporal-mcp-server/
• Docker - https://hub.docker.com/r/mcp/temporal

Tools

Workflow Execution

• start_workflow - Start a new Temporal workflow execution with specified parameters, workflow ID, and task queue
• get_workflow_result - Retrieve the result of a completed workflow execution
• describe_workflow - Get detailed information about a workflow execution including status, timing, and metadata
• list_workflows - List workflow executions based on a query filter with pagination support (limit/skip)
• get_workflow_history - Retrieve the complete event history of a workflow execution
• get_workflow_event - Retrieve a single workflow history event with decoded payload fields when present

Workflow Control

• query_workflow - Query a running workflow for its current state without affecting execution
• signal_workflow - Send a signal to a running workflow to change its behavior or provide data
• cancel_workflow - Request cancellation of a running workflow execution
• terminate_workflow - Forcefully terminate a workflow execution with a reason
• continue_as_new - Signal a workflow to continue as new (restart with new inputs while preserving history link)

Batch Operations

• batch_signal - Send a signal to multiple workflows matching a query (configurable batch size)
• batch_cancel - Cancel multiple workflows matching a query (configurable batch size)
• batch_terminate - Terminate multiple workflows matching a query with a specified reason (configurable batch size)

Schedule Management

• create_schedule - Create a new schedule for periodic workflow execution using cron expressions
• list_schedules - List all schedules with pagination support (limit/skip)
• describe_schedule - Get detailed configuration and runtime information about a schedule, including its spec, action, state, recent executions, and upcoming action times
• pause_schedule - Pause a schedule to temporarily stop workflow executions
• unpause_schedule - Resume a paused schedule
• delete_schedule - Permanently delete a schedule
• trigger_schedule - Manually trigger a scheduled workflow immediately

Temporal Documentation

For more information about Temporal, refer to the official Temporal documentation:

• Temporal Documentation: https://docs.temporal.io/
• Workflows: https://docs.temporal.io/workflows
• Activities: https://docs.temporal.io/activities
• Python SDK: https://docs.temporal.io/dev-guide/python

VS Code MCP Config

Add a .vscode/mcp.json file to your workspace. Choose the approach that fits your setup.

Docker (environment variables)

Recommended when running via Docker. Configuration is passed through environment variables.

{
  "servers": {
    "temporal": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "TEMPORAL_HOST",
        "-e", "TEMPORAL_NAMESPACE",
        "-e", "TEMPORAL_TLS_ENABLED",
        "-e", "TEMPORAL_TLS_CLIENT_CERT_PATH",
        "-e", "TEMPORAL_TLS_CLIENT_KEY_PATH",
        "-e", "TEMPORAL_API_KEY",
        "mcp/temporal"
      ],
      "env": {
        "TEMPORAL_HOST": "localhost:7233",
        "TEMPORAL_NAMESPACE": "default",
        "TEMPORAL_TLS_ENABLED": "false",
        "TEMPORAL_TLS_CLIENT_CERT_PATH": "/path/to/client.pem",
        "TEMPORAL_TLS_CLIENT_KEY_PATH": "/path/to/client.key",
        "TEMPORAL_API_KEY": "your-api-key"
      }
    }
  }
}

Python via uvx (CLI arguments)

Recommended when running from PyPI via uvx. No local install required — uvx fetches and runs the package automatically. Configuration is passed as CLI arguments.

{
  "servers": {
    "temporal": {
      "command": "uvx",
      "args": [
        "temporal-mcp-server",
        "--host", "localhost:7233",
        "--namespace", "default",
        "--tls-enabled", "false",
        "--tls-cert", "/path/to/client.pem",
        "--tls-key", "/path/to/client.key",
        "--api-key", "your-api-key"
      ]
    }
  }
}

Configuration Options

CLI arguments take precedence over environment variables. When TEMPORAL_API_KEY is set, TLS is enabled automatically. When mTLS cert/key paths are provided, TLS is also enabled automatically.

Every tool accepts an optional namespace argument. If omitted, the server uses --namespace, then TEMPORAL_NAMESPACE, then default. Runtime overrides are disabled by default: set TEMPORAL_ALLOWED_NAMESPACES to a comma-separated allowlist such as default,payments, or set it to * to permit any namespace reachable through the configured Temporal host and credentials. A finite allowlist must include the configured default namespace.

Remote codec

When payloads or failure messages are encrypted by a codec server, set --codec-endpoint and the server decodes them through it: get_workflow_event shows decoded inputs, results and failure details, and get_workflow_history shows decoded failure messages and stack traces, including every cause in the chain. Without a codec nothing is decoded and no requests are made to a codec server.

Each request carries the namespace in an X-Namespace header, and requests for other allowed namespaces use their own. Only http and https endpoints are accepted, and redirects are never followed, so credentials are not forwarded to another host.

If the codec server requires authentication, pass the full header value with --codec-auth "Bearer <token>" (or TEMPORAL_CODEC_AUTH). You are responsible for renewing it.

Development

Running Tests

Install development dependencies:

pip install -r requirements-dev.txt

Run the test suite:

pytest test.py -v

Building the Docker Image

docker build -t mcp/temporal:latest .