Provides tools for listing, describing, and fetching the event history of Temporal workflow executions, enabling LLM-assisted debugging of Temporal workflows.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@temporal-mcp list workflows with status Running"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server for debugging Temporal workflows. This server enables LLM-assisted debugging by exposing Temporal workflow inspection tools.
list_workflows - List workflow executions with optional query filter
describe_workflow - Get detailed info about a specific workflow
get_workflow_history - Fetch event history for debugging
Related MCP server: prefect-mcp-server
Python 3.11+
uv for dependency management
A running Temporal server (local or cloud)
# Clone the repository
git clone <repo-url>
cd temporal-mcp
# Create virtual environment and install
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"Set environment variables to configure the Temporal connection:
Variable | Description | Default |
| Temporal server address |
|
| Temporal namespace |
|
| Path to TLS certificate (for Cloud) | - |
| Path to TLS key (for Cloud) | - |
| API key (for Cloud) | - |
If you have Temporal running (locally or remotely):
# Connect to Temporal on host machine
docker compose up
# Or specify a custom address
TEMPORAL_ADDRESS=my-temporal:7233 docker compose upFor Temporal Cloud with mTLS certificates:
# Place your certificates in a certs/ directory, then:
docker compose up
# After uncommenting the TLS environment variables in docker-compose.ymlOr with API key:
TEMPORAL_ADDRESS=your-ns.tmprl.cloud:7233 \
TEMPORAL_NAMESPACE=your-ns \
TEMPORAL_API_KEY=your-key \
docker compose upInstall and run directly with Python:
For Temporal Cloud, set the appropriate environment variables:
export TEMPORAL_ADDRESS="your-namespace.tmprl.cloud:7233"
export TEMPORAL_NAMESPACE="your-namespace"
export TEMPORAL_API_KEY="your-api-key"Or with TLS certificates:
export TEMPORAL_ADDRESS="your-namespace.tmprl.cloud:7233"
export TEMPORAL_NAMESPACE="your-namespace"
export TEMPORAL_TLS_CERT="/path/to/cert.pem"
export TEMPORAL_TLS_KEY="/path/to/key.pem"Add to your Cursor MCP settings (~/.cursor/mcp.json):
{
"mcpServers": {
"temporal": {
"command": "uv",
"args": ["run", "temporal-mcp"],
"cwd": "/path/to/temporal-mcp",
"env": {
"TEMPORAL_ADDRESS": "localhost:7233",
"TEMPORAL_NAMESPACE": "default"
}
}
}
}temporal-mcpList workflow executions with optional filtering.
list_workflows(query="", limit=10)query: Optional Temporal list filter syntax (e.g., WorkflowType="MyWorkflow" AND ExecutionStatus="Running")
limit: Maximum workflows to return (default 10, max 100)
Get detailed information about a specific workflow.
describe_workflow(workflow_id, run_id="")workflow_id: The workflow ID to describe
run_id: Optional run ID (uses latest if not specified)
Fetch the event history for a workflow execution.
get_workflow_history(workflow_id, run_id="", max_events=100)workflow_id: The workflow ID
run_id: Optional run ID (uses latest if not specified)
max_events: Maximum events to return (default 100, max 1000)
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"# Format code
black src tests
# Lint
ruff check src tests
# Type check
mypy src
# Run tests
pytest
# Run all checks
black src tests && ruff check src tests && mypy src && pytesttemporal-mcp/
├── pyproject.toml # Project config and dependencies
├── Dockerfile
├── docker-compose.yml # Docker setup for MCP server
├── README.md
├── src/temporal_mcp/
│ ├── __init__.py
│ ├── server.py # MCP server and tool definitions
│ ├── client.py # Temporal client wrapper
│ ├── config.py # Environment-based configuration
│ └── models.py # Pydantic models
├── tests/
│ ├── conftest.py # Test fixtures
│ ├── test_config.py
│ ├── test_client.py
│ └── test_server.py
└── docs/
└── PLAN.txtMIT
Get detailed information about a specific workflow execution.
Args: workflow_id: The workflow ID to describe run_id: Optional run ID (uses latest run if not specified)
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | No | ||
| workflow_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. 'Get' implies a read-only operation, and the run_id note adds useful default behavior. However, it does not explicitly state side-effect-freeness, failure behavior, or other operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the main purpose, followed by a clean parameter block. Every line earns its place; there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists, so return-value details are not required here. However, the description does not mention how this tool relates to list_workflows or get_workflow_history, and it lacks any explicit selection guidance. For a tool with close siblings, this is a notable completeness gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for both parameters. It does: workflow_id is identified as the target workflow, and run_id is explained as optional with a clear default behavior. This fully covers parameter meaning despite the sparse schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb and resource: 'Get detailed information about a specific workflow execution.' This clearly communicates the tool's core function. It does not explicitly name or distinguish itself from sibling tools, so it stops short of a top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to choose this tool over list_workflows or get_workflow_history. The only usage hint is parameter-level ('uses latest run if not specified'), which does not help an agent decide between related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the event history for a workflow execution.
Args: workflow_id: The workflow ID run_id: Optional run ID (uses latest run if not specified) max_events: Maximum number of events to return (default 100, max 1000)
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | No | ||
| max_events | No | ||
| workflow_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that run_id is optional and defaults to the latest run, and that max_events defaults to 100 with a maximum of 1000. However, it does not mention output ordering, error behavior, or potential cost/rate concerns. The information provided is useful but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description opens with a clear one-sentence purpose, followed by a compact Args block that gives each parameter purpose and constraints. There is no filler or repetition; every detail earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return values need not be detailed. The description explains all parameters and their behaviors, which is sufficient for a simple read-only history tool. Minor gaps such as ordering or error handling prevent a perfect score, but overall the definition is complete enough for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates fully by explaining each parameter: workflow_id is the required identifier, run_id is optional and falls back to latest run, and max_events has both a default and maximum. This adds substantial meaning beyond the raw schema types and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the function: 'Get the event history for a workflow execution.' The verb 'get' and the resource 'event history' are specific. While it doesn't explicitly contrast with siblings like list_workflows or describe_workflow, the resource is distinct enough that an agent can infer the difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to choose this tool over its siblings. The description explains parameter behavior (e.g., run_id defaults to latest) but does not mention alternatives or conditions for use. The agent is left to infer that get_workflow_history is for execution events rather than listing or describing workflows.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List workflow executions from Temporal.
Args: query: Optional query filter (Temporal list filter syntax). Example: 'WorkflowType="MyWorkflow" AND ExecutionStatus="Running"' limit: Maximum number of workflows to return (default 10, max 100)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It does add useful details: this is a list/read-style operation with an optional Temporal query filter and a limit between 10 and 100. However, it omits pagination, ordering, and whether exceeding the max errors or clamps. It does not contradict any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose is stated in one front-loaded sentence, followed by a compact Args block. There is no filler, and every sentence contributes useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter list tool, the description covers the core call semantics and can rely on the output schema for return values. However, it does not mention how this tool relates to the siblings and omits behavior like pagination or ordering, so an agent choosing among sibling tools may be under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must carry parameter meaning. It fully compensates by explaining query as a Temporal list filter with a concrete syntax example and by specifying limit's default and maximum. Both parameters receive semantic value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'List workflow executions from Temporal', which is a specific verb and resource. This clearly distinguishes it from the sibling tools describe_workflow and get_workflow_history, even though those are not explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to use query and limit but provides no explicit guidance about when to choose this tool over describe_workflow or get_workflow_history. There are no exclusions, prerequisites, or alternative routing hints beyond the implied meaning of 'list'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct aspect of workflow inspection: list_workflows returns filtered overviews, describe_workflow provides detailed state for a specific execution, and get_workflow_history returns the event sequence. There is no meaningful overlap between the tools.
All tool names follow a consistent lowercase verb_noun pattern: list_workflows, describe_workflow, get_workflow_history. The naming style is uniform and predictable.
Three tools form a focused read-only workflow inspection surface for Temporal. Each tool earns its place, and the count is well-scoped for the apparent purpose.
The set covers core inspection operations well, but it lacks common workflow management operations such as start, signal, terminate, or query. For a read-only observability server this would be complete, but for a general temporal-mcp toolset it has notable gaps.
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