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python-sdk/docs/experimental/tasks-server.md

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# Server Task Implementation
!!! warning "Deprecated"
The experimental tasks API is deprecated and will be removed in mcp 2.0.
Tasks (SEP-1686) were removed from the MCP specification and are expected
to return as a separate MCP extension in a future release.
This guide covers implementing task support in MCP servers, from basic setup to advanced patterns like elicitation and sampling within tasks.
## Quick Start
The simplest way to add task support:
```python
from mcp.server import Server
from mcp.server.experimental.task_context import ServerTaskContext
from mcp.types import CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution, TASK_REQUIRED
server = Server("my-server")
server.experimental.enable_tasks() # Registers all task handlers automatically
@server.list_tools()
async def list_tools():
return [
Tool(
name="process_data",
description="Process data asynchronously",
inputSchema={"type": "object", "properties": {"input": {"type": "string"}}},
execution=ToolExecution(taskSupport=TASK_REQUIRED),
)
]
@server.call_tool()
async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
if name == "process_data":
return await handle_process_data(arguments)
return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
async def handle_process_data(arguments: dict) -> CreateTaskResult:
ctx = server.request_context
ctx.experimental.validate_task_mode(TASK_REQUIRED)
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Processing...")
result = arguments.get("input", "").upper()
return CallToolResult(content=[TextContent(type="text", text=result)])
return await ctx.experimental.run_task(work)
```
That's it. `enable_tasks()` automatically:
- Creates an in-memory task store
- Registers handlers for `tasks/get`, `tasks/result`, `tasks/list`, `tasks/cancel`
- Updates server capabilities
## Task Visibility
Task IDs generated by `run_task()` embed an opaque marker identifying the session that
created the task, and the default handlers use it to restrict each session to its own
tasks: `tasks/get`, `tasks/result`, and `tasks/cancel` respond with "task not found" for
another session's task, and `tasks/list` returns only the requesting session's tasks. A
client that reconnects gets a new session and can no longer reach tasks it created on the
previous one.
A task ID has no session marker when it was passed to `run_task()` explicitly, when the
task was created directly through the `TaskStore`, or when the server runs in stateless
mode (each request gets a fresh session, so tasks must remain reachable across requests).
Such tasks are accessible to any requestor that presents the exact task ID, and are never
included in `tasks/list` responses because the server cannot tell which session they
belong to. Treat these task IDs as capabilities: generate them with enough entropy that
they cannot be guessed, share them only with the intended recipient, and prefer short
TTLs. Passing an explicit `task_id` to `run_task()` is deprecated for this reason.
To scope tasks to something other than the session — for example a user identity from your
authorization layer — register your own handlers with `@server.experimental.get_task()`,
`@server.experimental.get_task_result()`, `@server.experimental.list_tasks()`, and
`@server.experimental.cancel_task()` instead of relying on the defaults.
## Tool Declaration
Tools declare task support via the `execution.taskSupport` field:
```python
from mcp.types import Tool, ToolExecution, TASK_REQUIRED, TASK_OPTIONAL, TASK_FORBIDDEN
Tool(
name="my_tool",
inputSchema={"type": "object"},
execution=ToolExecution(taskSupport=TASK_REQUIRED), # or TASK_OPTIONAL, TASK_FORBIDDEN
)
```
| Value | Meaning |
|-------|---------|
| `TASK_REQUIRED` | Tool **must** be called as a task |
| `TASK_OPTIONAL` | Tool supports both sync and task execution |
| `TASK_FORBIDDEN` | Tool **cannot** be called as a task (default) |
Validate the request matches your tool's requirements:
```python
@server.call_tool()
async def handle_tool(name: str, arguments: dict):
ctx = server.request_context
if name == "required_task_tool":
ctx.experimental.validate_task_mode(TASK_REQUIRED) # Raises if not task mode
return await handle_as_task(arguments)
elif name == "optional_task_tool":
if ctx.experimental.is_task:
return await handle_as_task(arguments)
else:
return handle_sync(arguments)
```
## The run_task Pattern
`run_task()` is the recommended way to execute task work:
```python
async def handle_my_tool(arguments: dict) -> CreateTaskResult:
ctx = server.request_context
ctx.experimental.validate_task_mode(TASK_REQUIRED)
async def work(task: ServerTaskContext) -> CallToolResult:
# Your work here
return CallToolResult(content=[TextContent(type="text", text="Done")])
return await ctx.experimental.run_task(work)
```
**What `run_task()` does:**
1. Creates a task in the store
2. Spawns your work function in the background
3. Returns `CreateTaskResult` immediately
4. Auto-completes the task when your function returns
5. Auto-fails the task if your function raises
**The `ServerTaskContext` provides:**
- `task.task_id` - The task identifier
- `task.update_status(message)` - Update progress
- `task.complete(result)` - Explicitly complete (usually automatic)
- `task.fail(error)` - Explicitly fail
- `task.is_cancelled` - Check if cancellation requested
## Status Updates
Keep clients informed of progress:
```python
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Starting...")
for i, item in enumerate(items):
await task.update_status(f"Processing {i+1}/{len(items)}")
await process_item(item)
await task.update_status("Finalizing...")
return CallToolResult(content=[TextContent(type="text", text="Complete")])
```
Status messages appear in `tasks/get` responses, letting clients show progress to users.
## Elicitation Within Tasks
Tasks can request user input via elicitation. This transitions the task to `input_required` status.
### Form Elicitation
Collect structured data from the user:
```python
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Waiting for confirmation...")
result = await task.elicit(
message="Delete these files?",
requestedSchema={
"type": "object",
"properties": {
"confirm": {"type": "boolean"},
"reason": {"type": "string"},
},
"required": ["confirm"],
},
)
if result.action == "accept" and result.content.get("confirm"):
# User confirmed
return CallToolResult(content=[TextContent(type="text", text="Files deleted")])
else:
# User declined or cancelled
return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
```
### URL Elicitation
Direct users to external URLs for OAuth, payments, or other out-of-band flows:
```python
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Waiting for OAuth...")
result = await task.elicit_url(
message="Please authorize with GitHub",
url="https://github.com/login/oauth/authorize?client_id=...",
elicitation_id="oauth-github-123",
)
if result.action == "accept":
# User completed OAuth flow
return CallToolResult(content=[TextContent(type="text", text="Connected to GitHub")])
else:
return CallToolResult(content=[TextContent(type="text", text="OAuth cancelled")])
```
## Sampling Within Tasks
Tasks can request LLM completions from the client:
```python
from mcp.types import SamplingMessage, TextContent
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Generating response...")
result = await task.create_message(
messages=[
SamplingMessage(
role="user",
content=TextContent(type="text", text="Write a haiku about coding"),
)
],
max_tokens=100,
)
haiku = result.content.text if isinstance(result.content, TextContent) else "Error"
return CallToolResult(content=[TextContent(type="text", text=haiku)])
```
Sampling supports additional parameters:
```python
result = await task.create_message(
messages=[...],
max_tokens=500,
system_prompt="You are a helpful assistant",
temperature=0.7,
stop_sequences=["\n\n"],
model_preferences=ModelPreferences(hints=[ModelHint(name="claude-3")]),
)
```
## Cancellation Support
Check for cancellation in long-running work:
```python
async def work(task: ServerTaskContext) -> CallToolResult:
for i in range(1000):
if task.is_cancelled:
# Clean up and exit
return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
await task.update_status(f"Step {i}/1000")
await process_step(i)
return CallToolResult(content=[TextContent(type="text", text="Complete")])
```
The SDK's default cancel handler updates the task status. Your work function should check `is_cancelled` periodically.
## Custom Task Store
For production, implement `TaskStore` with persistent storage:
```python
from mcp.shared.experimental.tasks.store import TaskStore
from mcp.types import Task, TaskMetadata, Result
class RedisTaskStore(TaskStore):
def __init__(self, redis_client):
self.redis = redis_client
async def create_task(self, metadata: TaskMetadata, task_id: str | None = None) -> Task:
# Create and persist task
...
async def get_task(self, task_id: str) -> Task | None:
# Retrieve task from Redis
...
async def update_task(self, task_id: str, status: str | None = None, ...) -> Task:
# Update and persist
...
async def store_result(self, task_id: str, result: Result) -> None:
# Store result in Redis
...
async def get_result(self, task_id: str) -> Result | None:
# Retrieve result
...
# ... implement remaining methods
```
Use your custom store:
```python
store = RedisTaskStore(redis_client)
server.experimental.enable_tasks(store=store)
```
## Complete Example
A server with multiple task-supporting tools:
```python
from mcp.server import Server
from mcp.server.experimental.task_context import ServerTaskContext
from mcp.types import (
CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution,
SamplingMessage, TASK_REQUIRED,
)
server = Server("task-demo")
server.experimental.enable_tasks()
@server.list_tools()
async def list_tools():
return [
Tool(
name="confirm_action",
description="Requires user confirmation",
inputSchema={"type": "object", "properties": {"action": {"type": "string"}}},
execution=ToolExecution(taskSupport=TASK_REQUIRED),
),
Tool(
name="generate_text",
description="Generate text via LLM",
inputSchema={"type": "object", "properties": {"prompt": {"type": "string"}}},
execution=ToolExecution(taskSupport=TASK_REQUIRED),
),
]
async def handle_confirm_action(arguments: dict) -> CreateTaskResult:
ctx = server.request_context
ctx.experimental.validate_task_mode(TASK_REQUIRED)
action = arguments.get("action", "unknown action")
async def work(task: ServerTaskContext) -> CallToolResult:
result = await task.elicit(
message=f"Confirm: {action}?",
requestedSchema={
"type": "object",
"properties": {"confirm": {"type": "boolean"}},
"required": ["confirm"],
},
)
if result.action == "accept" and result.content.get("confirm"):
return CallToolResult(content=[TextContent(type="text", text=f"Executed: {action}")])
return CallToolResult(content=[TextContent(type="text", text="Cancelled")])
return await ctx.experimental.run_task(work)
async def handle_generate_text(arguments: dict) -> CreateTaskResult:
ctx = server.request_context
ctx.experimental.validate_task_mode(TASK_REQUIRED)
prompt = arguments.get("prompt", "Hello")
async def work(task: ServerTaskContext) -> CallToolResult:
await task.update_status("Generating...")
result = await task.create_message(
messages=[SamplingMessage(role="user", content=TextContent(type="text", text=prompt))],
max_tokens=200,
)
text = result.content.text if isinstance(result.content, TextContent) else "Error"
return CallToolResult(content=[TextContent(type="text", text=text)])
return await ctx.experimental.run_task(work)
@server.call_tool()
async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
if name == "confirm_action":
return await handle_confirm_action(arguments)
elif name == "generate_text":
return await handle_generate_text(arguments)
return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
```
## Error Handling in Tasks
Tasks handle errors automatically, but you can also fail explicitly:
```python
async def work(task: ServerTaskContext) -> CallToolResult:
try:
result = await risky_operation()
return CallToolResult(content=[TextContent(type="text", text=result)])
except PermissionError:
await task.fail("Access denied - insufficient permissions")
raise
except TimeoutError:
await task.fail("Operation timed out after 30 seconds")
raise
```
When `run_task()` catches an exception, it automatically:
1. Marks the task as `failed`
2. Sets `statusMessage` to the exception message
3. Propagates the exception (which is caught by the task group)
For custom error messages, call `task.fail()` before raising.
## HTTP Transport Example
For web applications, use the Streamable HTTP transport:
```python
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
import uvicorn
from starlette.applications import Starlette
from starlette.routing import Mount
from mcp.server import Server
from mcp.server.experimental.task_context import ServerTaskContext
from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
from mcp.types import (
CallToolResult, CreateTaskResult, TextContent, Tool, ToolExecution, TASK_REQUIRED,
)
server = Server("http-task-server")
server.experimental.enable_tasks()
@server.list_tools()
async def list_tools():
return [
Tool(
name="long_operation",
description="A long-running operation",
inputSchema={"type": "object", "properties": {"duration": {"type": "number"}}},
execution=ToolExecution(taskSupport=TASK_REQUIRED),
)
]
async def handle_long_operation(arguments: dict) -> CreateTaskResult:
ctx = server.request_context
ctx.experimental.validate_task_mode(TASK_REQUIRED)
duration = arguments.get("duration", 5)
async def work(task: ServerTaskContext) -> CallToolResult:
import anyio
for i in range(int(duration)):
await task.update_status(f"Step {i+1}/{int(duration)}")
await anyio.sleep(1)
return CallToolResult(content=[TextContent(type="text", text=f"Completed after {duration}s")])
return await ctx.experimental.run_task(work)
@server.call_tool()
async def handle_tool(name: str, arguments: dict) -> CallToolResult | CreateTaskResult:
if name == "long_operation":
return await handle_long_operation(arguments)
return CallToolResult(content=[TextContent(type="text", text=f"Unknown: {name}")], isError=True)
def create_app():
session_manager = StreamableHTTPSessionManager(app=server)
@asynccontextmanager
async def lifespan(app: Starlette) -> AsyncIterator[None]:
async with session_manager.run():
yield
return Starlette(
routes=[Mount("/mcp", app=session_manager.handle_request)],
lifespan=lifespan,
)
if __name__ == "__main__":
uvicorn.run(create_app(), host="127.0.0.1", port=8000)
```
## Testing Task Servers
Test task functionality with the SDK's testing utilities:
```python
import pytest
import anyio
from mcp.client.session import ClientSession
from mcp.types import CallToolResult
@pytest.mark.anyio
async def test_task_tool():
server_to_client_send, server_to_client_receive = anyio.create_memory_object_stream(10)
client_to_server_send, client_to_server_receive = anyio.create_memory_object_stream(10)
async def run_server():
await server.run(
client_to_server_receive,
server_to_client_send,
server.create_initialization_options(),
)
async def run_client():
async with ClientSession(server_to_client_receive, client_to_server_send) as session:
await session.initialize()
# Call the tool as a task
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
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