SEP-1686: Tasks (#1645)
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Max Isbey
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docs/experimental/tasks-server.md
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docs/experimental/tasks-server.md
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# Server Task Implementation
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!!! warning "Experimental"
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Tasks are an experimental feature. The API may change without notice.
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This guide covers implementing task support in MCP servers, from basic setup to advanced patterns like elicitation and sampling within tasks.
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## Quick Start
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The simplest way to add task support:
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```python
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from mcp.server import Server
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from mcp.server.experimental.task_context import ServerTaskContext
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from mcp.types import CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution, TASK_REQUIRED
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server = Server("my-server")
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server.experimental.enable_tasks() # Registers all task handlers automatically
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@server.list_tools()
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async def list_tools():
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return [
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Tool(
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name="process_data",
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description="Process data asynchronously",
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inputSchema={"type": "object", "properties": {"input": {"type": "string"}}},
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execution=ToolExecution(taskSupport=TASK_REQUIRED),
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)
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]
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@server.call_tool()
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async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
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if name == "process_data":
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return await handle_process_data(arguments)
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return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
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async def handle_process_data(arguments: dict) -> CreateTaskResult:
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ctx = server.request_context
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ctx.experimental.validate_task_mode(TASK_REQUIRED)
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Processing...")
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result = arguments.get("input", "").upper()
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return CallToolResult(content=[TextContent(type="text", text=result)])
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return await ctx.experimental.run_task(work)
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```
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That's it. `enable_tasks()` automatically:
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- Creates an in-memory task store
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- Registers handlers for `tasks/get`, `tasks/result`, `tasks/list`, `tasks/cancel`
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- Updates server capabilities
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## Tool Declaration
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Tools declare task support via the `execution.taskSupport` field:
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```python
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from mcp.types import Tool, ToolExecution, TASK_REQUIRED, TASK_OPTIONAL, TASK_FORBIDDEN
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Tool(
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name="my_tool",
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inputSchema={"type": "object"},
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execution=ToolExecution(taskSupport=TASK_REQUIRED), # or TASK_OPTIONAL, TASK_FORBIDDEN
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)
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```
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| Value | Meaning |
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|-------|---------|
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| `TASK_REQUIRED` | Tool **must** be called as a task |
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| `TASK_OPTIONAL` | Tool supports both sync and task execution |
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| `TASK_FORBIDDEN` | Tool **cannot** be called as a task (default) |
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Validate the request matches your tool's requirements:
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```python
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@server.call_tool()
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async def handle_tool(name: str, arguments: dict):
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ctx = server.request_context
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if name == "required_task_tool":
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ctx.experimental.validate_task_mode(TASK_REQUIRED) # Raises if not task mode
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return await handle_as_task(arguments)
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elif name == "optional_task_tool":
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if ctx.experimental.is_task:
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return await handle_as_task(arguments)
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else:
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return handle_sync(arguments)
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```
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## The run_task Pattern
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`run_task()` is the recommended way to execute task work:
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```python
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async def handle_my_tool(arguments: dict) -> CreateTaskResult:
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ctx = server.request_context
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ctx.experimental.validate_task_mode(TASK_REQUIRED)
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async def work(task: ServerTaskContext) -> CallToolResult:
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# Your work here
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return CallToolResult(content=[TextContent(type="text", text="Done")])
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return await ctx.experimental.run_task(work)
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```
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**What `run_task()` does:**
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1. Creates a task in the store
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2. Spawns your work function in the background
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3. Returns `CreateTaskResult` immediately
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4. Auto-completes the task when your function returns
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5. Auto-fails the task if your function raises
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**The `ServerTaskContext` provides:**
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- `task.task_id` - The task identifier
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- `task.update_status(message)` - Update progress
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- `task.complete(result)` - Explicitly complete (usually automatic)
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- `task.fail(error)` - Explicitly fail
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- `task.is_cancelled` - Check if cancellation requested
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## Status Updates
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Keep clients informed of progress:
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```python
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Starting...")
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for i, item in enumerate(items):
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await task.update_status(f"Processing {i+1}/{len(items)}")
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await process_item(item)
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await task.update_status("Finalizing...")
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return CallToolResult(content=[TextContent(type="text", text="Complete")])
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```
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Status messages appear in `tasks/get` responses, letting clients show progress to users.
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## Elicitation Within Tasks
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Tasks can request user input via elicitation. This transitions the task to `input_required` status.
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### Form Elicitation
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Collect structured data from the user:
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```python
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Waiting for confirmation...")
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result = await task.elicit(
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message="Delete these files?",
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requestedSchema={
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"type": "object",
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"properties": {
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"confirm": {"type": "boolean"},
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"reason": {"type": "string"},
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},
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"required": ["confirm"],
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},
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)
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if result.action == "accept" and result.content.get("confirm"):
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# User confirmed
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return CallToolResult(content=[TextContent(type="text", text="Files deleted")])
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else:
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# User declined or cancelled
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return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
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```
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### URL Elicitation
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Direct users to external URLs for OAuth, payments, or other out-of-band flows:
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```python
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Waiting for OAuth...")
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result = await task.elicit_url(
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message="Please authorize with GitHub",
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url="https://github.com/login/oauth/authorize?client_id=...",
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elicitation_id="oauth-github-123",
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)
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if result.action == "accept":
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# User completed OAuth flow
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return CallToolResult(content=[TextContent(type="text", text="Connected to GitHub")])
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else:
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return CallToolResult(content=[TextContent(type="text", text="OAuth cancelled")])
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```
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## Sampling Within Tasks
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Tasks can request LLM completions from the client:
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```python
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from mcp.types import SamplingMessage, TextContent
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Generating response...")
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result = await task.create_message(
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messages=[
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SamplingMessage(
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role="user",
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content=TextContent(type="text", text="Write a haiku about coding"),
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)
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],
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max_tokens=100,
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)
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haiku = result.content.text if isinstance(result.content, TextContent) else "Error"
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return CallToolResult(content=[TextContent(type="text", text=haiku)])
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```
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Sampling supports additional parameters:
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```python
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result = await task.create_message(
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messages=[...],
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max_tokens=500,
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system_prompt="You are a helpful assistant",
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temperature=0.7,
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stop_sequences=["\n\n"],
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model_preferences=ModelPreferences(hints=[ModelHint(name="claude-3")]),
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)
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```
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## Cancellation Support
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Check for cancellation in long-running work:
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```python
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async def work(task: ServerTaskContext) -> CallToolResult:
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for i in range(1000):
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if task.is_cancelled:
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# Clean up and exit
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return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
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await task.update_status(f"Step {i}/1000")
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await process_step(i)
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return CallToolResult(content=[TextContent(type="text", text="Complete")])
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```
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The SDK's default cancel handler updates the task status. Your work function should check `is_cancelled` periodically.
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## Custom Task Store
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For production, implement `TaskStore` with persistent storage:
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```python
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from mcp.shared.experimental.tasks.store import TaskStore
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from mcp.types import Task, TaskMetadata, Result
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class RedisTaskStore(TaskStore):
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def __init__(self, redis_client):
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self.redis = redis_client
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async def create_task(self, metadata: TaskMetadata, task_id: str | None = None) -> Task:
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# Create and persist task
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...
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async def get_task(self, task_id: str) -> Task | None:
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# Retrieve task from Redis
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...
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async def update_task(self, task_id: str, status: str | None = None, ...) -> Task:
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# Update and persist
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...
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async def store_result(self, task_id: str, result: Result) -> None:
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# Store result in Redis
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...
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async def get_result(self, task_id: str) -> Result | None:
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# Retrieve result
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...
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# ... implement remaining methods
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```
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Use your custom store:
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```python
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store = RedisTaskStore(redis_client)
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server.experimental.enable_tasks(store=store)
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```
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## Complete Example
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A server with multiple task-supporting tools:
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```python
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from mcp.server import Server
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from mcp.server.experimental.task_context import ServerTaskContext
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from mcp.types import (
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CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution,
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SamplingMessage, TASK_REQUIRED,
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)
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server = Server("task-demo")
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server.experimental.enable_tasks()
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@server.list_tools()
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async def list_tools():
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return [
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Tool(
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name="confirm_action",
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description="Requires user confirmation",
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inputSchema={"type": "object", "properties": {"action": {"type": "string"}}},
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execution=ToolExecution(taskSupport=TASK_REQUIRED),
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),
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Tool(
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name="generate_text",
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description="Generate text via LLM",
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inputSchema={"type": "object", "properties": {"prompt": {"type": "string"}}},
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execution=ToolExecution(taskSupport=TASK_REQUIRED),
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),
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]
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async def handle_confirm_action(arguments: dict) -> CreateTaskResult:
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ctx = server.request_context
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ctx.experimental.validate_task_mode(TASK_REQUIRED)
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action = arguments.get("action", "unknown action")
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async def work(task: ServerTaskContext) -> CallToolResult:
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result = await task.elicit(
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message=f"Confirm: {action}?",
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requestedSchema={
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"type": "object",
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"properties": {"confirm": {"type": "boolean"}},
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"required": ["confirm"],
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},
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)
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if result.action == "accept" and result.content.get("confirm"):
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return CallToolResult(content=[TextContent(type="text", text=f"Executed: {action}")])
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return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
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return await ctx.experimental.run_task(work)
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async def handle_generate_text(arguments: dict) -> CreateTaskResult:
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ctx = server.request_context
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ctx.experimental.validate_task_mode(TASK_REQUIRED)
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prompt = arguments.get("prompt", "Hello")
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async def work(task: ServerTaskContext) -> CallToolResult:
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await task.update_status("Generating...")
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result = await task.create_message(
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messages=[SamplingMessage(role="user", content=TextContent(type="text", text=prompt))],
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max_tokens=200,
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)
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text = result.content.text if isinstance(result.content, TextContent) else "Error"
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return CallToolResult(content=[TextContent(type="text", text=text)])
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return await ctx.experimental.run_task(work)
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@server.call_tool()
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async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
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if name == "confirm_action":
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return await handle_confirm_action(arguments)
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elif name == "generate_text":
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return await handle_generate_text(arguments)
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return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
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```
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## Error Handling in Tasks
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Tasks handle errors automatically, but you can also fail explicitly:
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```python
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async def work(task: ServerTaskContext) -> CallToolResult:
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try:
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result = await risky_operation()
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return CallToolResult(content=[TextContent(type="text", text=result)])
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except PermissionError:
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await task.fail("Access denied - insufficient permissions")
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raise
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except TimeoutError:
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await task.fail("Operation timed out after 30 seconds")
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raise
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```
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When `run_task()` catches an exception, it automatically:
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1. Marks the task as `failed`
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2. Sets `statusMessage` to the exception message
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3. Propagates the exception (which is caught by the task group)
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For custom error messages, call `task.fail()` before raising.
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## HTTP Transport Example
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For web applications, use the Streamable HTTP transport:
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```python
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from collections.abc import AsyncIterator
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from contextlib import asynccontextmanager
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import uvicorn
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from starlette.applications import Starlette
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from starlette.routing import Mount
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from mcp.server import Server
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from mcp.server.experimental.task_context import ServerTaskContext
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from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
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from mcp.types import (
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CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution, TASK_REQUIRED,
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)
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server = Server("http-task-server")
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server.experimental.enable_tasks()
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@server.list_tools()
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async def list_tools():
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return [
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Tool(
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name="long_operation",
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description="A long-running operation",
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inputSchema={"type": "object", "properties": {"duration": {"type": "number"}}},
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execution=ToolExecution(taskSupport=TASK_REQUIRED),
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)
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]
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async def handle_long_operation(arguments: dict) -> CreateTaskResult:
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ctx = server.request_context
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ctx.experimental.validate_task_mode(TASK_REQUIRED)
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duration = arguments.get("duration", 5)
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async def work(task: ServerTaskContext) -> CallToolResult:
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import anyio
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for i in range(int(duration)):
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await task.update_status(f"Step {i+1}/{int(duration)}")
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await anyio.sleep(1)
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return CallToolResult(content=[TextContent(type="text", text=f"Completed after {duration}s")])
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return await ctx.experimental.run_task(work)
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@server.call_tool()
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async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
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if name == "long_operation":
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return await handle_long_operation(arguments)
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return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
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def create_app():
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session_manager = StreamableHTTPSessionManager(app=server)
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@asynccontextmanager
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async def lifespan(app: Starlette) -> AsyncIterator[None]:
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async with session_manager.run():
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yield
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return Starlette(
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routes=[Mount("/mcp", app=session_manager.handle_request)],
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lifespan=lifespan,
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)
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if __name__ == "__main__":
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uvicorn.run(create_app(), host="127.0.0.1", port=8000)
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```
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## Testing Task Servers
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Test task functionality with the SDK's testing utilities:
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```python
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import pytest
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import anyio
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from mcp.client.session import ClientSession
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from mcp.types import CallToolResult
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@pytest.mark.anyio
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async def test_task_tool():
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server_to_client_send, server_to_client_receive = anyio.create_memory_object_stream(10)
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client_to_server_send, client_to_server_receive = anyio.create_memory_object_stream(10)
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async def run_server():
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await server.run(
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client_to_server_receive,
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server_to_client_send,
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server.create_initialization_options(),
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)
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async def run_client():
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async with ClientSession(server_to_client_receive, client_to_server_send) as session:
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await session.initialize()
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# Call the tool as a task
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result = await session.experimental.call_tool_as_task("my_tool", {"arg": "value"})
|
||||
task_id = result.task.taskId
|
||||
assert result.task.status == "working"
|
||||
|
||||
# Poll until complete
|
||||
async for status in session.experimental.poll_task(task_id):
|
||||
if status.status in ("completed", "failed"):
|
||||
break
|
||||
|
||||
# Get result
|
||||
final = await session.experimental.get_task_result(task_id, CallToolResult)
|
||||
assert len(final.content) > 0
|
||||
|
||||
async with anyio.create_task_group() as tg:
|
||||
tg.start_soon(run_server)
|
||||
tg.start_soon(run_client)
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Keep Work Functions Focused
|
||||
|
||||
```python
|
||||
# Good: focused work function
|
||||
async def work(task: ServerTaskContext) -> CallToolResult:
|
||||
await task.update_status("Validating...")
|
||||
validate_input(arguments)
|
||||
|
||||
await task.update_status("Processing...")
|
||||
result = await process_data(arguments)
|
||||
|
||||
return CallToolResult(content=[TextContent(type="text", text=result)])
|
||||
```
|
||||
|
||||
### Check Cancellation in Loops
|
||||
|
||||
```python
|
||||
async def work(task: ServerTaskContext) -> CallToolResult:
|
||||
results = []
|
||||
for item in large_dataset:
|
||||
if task.is_cancelled:
|
||||
return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
|
||||
|
||||
results.append(await process(item))
|
||||
|
||||
return CallToolResult(content=[TextContent(type="text", text=str(results))])
|
||||
```
|
||||
|
||||
### Use Meaningful Status Messages
|
||||
|
||||
```python
|
||||
async def work(task: ServerTaskContext) -> CallToolResult:
|
||||
await task.update_status("Connecting to database...")
|
||||
db = await connect()
|
||||
|
||||
await task.update_status("Fetching records (0/1000)...")
|
||||
for i, record in enumerate(records):
|
||||
if i % 100 == 0:
|
||||
await task.update_status(f"Processing records ({i}/1000)...")
|
||||
await process(record)
|
||||
|
||||
await task.update_status("Finalizing results...")
|
||||
return CallToolResult(content=[TextContent(type="text", text="Done")])
|
||||
```
|
||||
|
||||
### Handle Elicitation Responses
|
||||
|
||||
```python
|
||||
async def work(task: ServerTaskContext) -> CallToolResult:
|
||||
result = await task.elicit(message="Continue?", requestedSchema={...})
|
||||
|
||||
match result.action:
|
||||
case "accept":
|
||||
# User accepted, process content
|
||||
return await process_accepted(result.content)
|
||||
case "decline":
|
||||
# User explicitly declined
|
||||
return CallToolResult(content=[TextContent(type="text", text="User declined")])
|
||||
case "cancel":
|
||||
# User cancelled the elicitation
|
||||
return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Client Usage](tasks-client.md) - Learn how clients interact with task servers
|
||||
- [Tasks Overview](tasks.md) - Review lifecycle and concepts
|
||||
Loading…
Reference in a new issue