Dependency Injection - Fastmcp

import { VersionBadge } from "/snippets/version-badge.mdx";

FastMCP uses dependency injection to provide runtime values to your tools, resources, and prompts. Instead of passing context through every layer of your code, you declare what you need as parameter defaults—FastMCP resolves them automatically when your function runs.

The dependency injection system is powered by uncalled-for, the same dependency engine used by Docket. Core DI features like Depends() and CurrentContext() work without installing Docket. Background task execution and task-specific dependencies such as CurrentDocket() and CurrentWorker() require fastmcp[tasks]. For comprehensive coverage of dependency patterns, see the Docket dependency documentation.

<Note> Dependency parameters are automatically excluded from the MCP schema—clients never see them as callable parameters. This separation keeps your function signatures clean while giving you access to the runtime context you need. </Note>

How Dependency Injection Works

Dependency injection in FastMCP follows a simple pattern: declare a parameter with a recognized type annotation or a dependency default value, and FastMCP injects the resolved value at runtime.

python

from fastmcp import FastMCP
from fastmcp.server.context import Context

mcp = FastMCP("Demo")


@mcp.tool
async def my_tool(query: str, ctx: Context) -> str:
    await ctx.info(f"Processing: {query}")
    return f"Results for: {query}"

When a client calls my_tool, they only see query as a parameter. The ctx parameter is injected automatically because it has a Context type annotation—FastMCP recognizes this and provides the active context for the request.

This works identically for tools, resources, resource templates, and prompts.

Explicit Dependencies with CurrentContext

For more explicit code, you can use CurrentContext() as a default value instead of relying on the type annotation:

python

from fastmcp import FastMCP
from fastmcp.dependencies import CurrentContext
from fastmcp.server.context import Context

mcp = FastMCP("Demo")


@mcp.tool
async def my_tool(query: str, ctx: Context = CurrentContext()) -> str:
    await ctx.info(f"Processing: {query}")
    return f"Results for: {query}"

Both approaches work identically. The type-annotation approach is more concise; the explicit CurrentContext() approach makes the dependency injection visible in the signature.

Built-in Dependencies

MCP Context

The MCP Context provides logging, progress reporting, resource access, and other request-scoped operations. See MCP Context for the full API.

Dependency injection: Use a Context type annotation (FastMCP injects automatically) or CurrentContext():

python

from fastmcp import FastMCP
from fastmcp.server.context import Context

mcp = FastMCP("Demo")


@mcp.tool
async def process_data(data: str, ctx: Context) -> str:
    await ctx.info(f"Processing: {data}")
    return "Done"


# Or explicitly with CurrentContext()
from fastmcp.dependencies import CurrentContext

@mcp.tool
async def process_data(data: str, ctx: Context = CurrentContext()) -> str:
    ...

Function: Use get_context() in helper functions or middleware:

python

from fastmcp.server.dependencies import get_context

async def log_something(message: str):
    ctx = get_context()
    await ctx.info(message)

Server Instance

Access the FastMCP server instance for introspection or server-level configuration.

Dependency injection: Use CurrentFastMCP():

python

from fastmcp import FastMCP
from fastmcp.dependencies import CurrentFastMCP

mcp = FastMCP("Demo")


@mcp.tool
async def server_info(server: FastMCP = CurrentFastMCP()) -> str:
    return f"Server: {server.name}"

Function: Use get_server():

python

from fastmcp.server.dependencies import get_server

def get_server_name() -> str:
    return get_server().name

HTTP Request

Access the Starlette Request when running over HTTP transports (SSE or Streamable HTTP).

Dependency injection: Use CurrentRequest():

python

from fastmcp import FastMCP
from fastmcp.dependencies import CurrentRequest
from starlette.requests import Request

mcp = FastMCP("Demo")


@mcp.tool
async def client_info(request: Request = CurrentRequest()) -> dict:
    return {
        "user_agent": request.headers.get("user-agent", "Unknown"),
        "client_ip": request.client.host if request.client else "Unknown",
    }

Function: Use get_http_request():

python

from fastmcp.server.dependencies import get_http_request

def get_client_ip() -> str:
    request = get_http_request()
    return request.client.host if request.client else "Unknown"

<Note> Both raise `RuntimeError` when called outside an HTTP context (e.g., STDIO transport, or inside a background task — there is no live request object to reconstruct there). Use HTTP Headers below if you need graceful fallback, including inside background tasks. </Note>

HTTP Headers

Access HTTP headers with graceful fallback. When a background task originates from an HTTP request, FastMCP restores the originating headers inside the worker. When no HTTP request is available, this returns an empty dictionary, making it safe for code that might run over any transport.

Dependency injection: Use CurrentHeaders():

python

from fastmcp import FastMCP
from fastmcp.dependencies import CurrentHeaders

mcp = FastMCP("Demo")


@mcp.tool
async def get_auth_type(headers: dict = CurrentHeaders()) -> str:
    auth = headers.get("authorization", "")
    return "Bearer" if auth.startswith("Bearer ") else "None"

Function: Use get_http_headers():

python

from fastmcp.server.dependencies import get_http_headers

def get_user_agent() -> str:
    headers = get_http_headers()
    return headers.get("user-agent", "Unknown")

By default, problematic headers like host and content-length are excluded. Use get_http_headers(include_all=True) to include all headers.

Access Token

Access the authenticated user's token when your server uses authentication.

Dependency injection: Use CurrentAccessToken() (raises if not authenticated):

python

from fastmcp import FastMCP
from fastmcp.dependencies import CurrentAccessToken
from fastmcp.server.auth import AccessToken

mcp = FastMCP("Demo")


@mcp.tool
async def get_user_id(token: AccessToken = CurrentAccessToken()) -> str:
    return token.claims.get("sub", "unknown")

Function: Use get_access_token() (returns None if not authenticated):

python

from fastmcp.server.dependencies import get_access_token

@mcp.tool
async def get_user_info() -> dict:
    token = get_access_token()
    if token is None:
        return {"authenticated": False}
    return {"authenticated": True, "user": token.claims.get("sub")}

The AccessToken object provides:

client_id: The OAuth client identifier
scopes: List of granted permission scopes
expires_at: Token expiration timestamp (if available)
claims: Dictionary of all token claims (JWT claims or provider-specific data)

Token Claims

When you need just one specific value from the token—like a user ID or tenant identifier—TokenClaim() extracts it directly without needing the full token object.

python

from fastmcp import FastMCP
from fastmcp.server.dependencies import TokenClaim

mcp = FastMCP("Demo")


@mcp.tool
async def add_expense(
    amount: float,
    user_id: str = TokenClaim("oid"),  # Azure object ID
) -> dict:
    await db.insert({"user_id": user_id, "amount": amount})
    return {"status": "created", "user_id": user_id}

TokenClaim() raises a RuntimeError if the claim doesn't exist, listing available claims to help with debugging.

Common claims vary by identity provider:

Provider	User ID Claim	Email Claim	Name Claim
Azure/Entra	`oid`	`email`	`name`
GitHub	`sub`	`email`	`name`
Google	`sub`	`email`	`name`
Auth0	`sub`	`email`	`name`

Background Task Dependencies

For background task execution, FastMCP provides dependencies that integrate with Docket. CurrentDocket() and CurrentWorker() require installing fastmcp[tasks]; Progress() also works during immediate foreground execution with an in-memory tracker, and delegates to Docket progress when a Docket worker context is active.

python

from fastmcp import FastMCP
from fastmcp.dependencies import Progress
from fastmcp_tasks.dependencies import CurrentDocket, CurrentWorker

mcp = FastMCP("Task Demo")


@mcp.tool(task=True)
async def long_running_task(
    data: str,
    docket=CurrentDocket(),
    worker=CurrentWorker(),
    progress=Progress(),
) -> str:
    await progress.set_total(100)

    for i in range(100):
        # Process chunk...
        await progress.increment()
        await progress.set_message(f"Processing chunk {i + 1}")

    return "Complete"

CurrentDocket(): Access the Docket instance for scheduling additional background work
CurrentWorker(): Access the worker processing tasks (name, concurrency settings)
Progress(): Track task progress with atomic updates

<Note> `CurrentDocket()` and `CurrentWorker()` require `pip install 'fastmcp[tasks]'`. They resolve once the server lifespan has initialized Docket, which happens as soon as any component on the server is task-enabled — so regular foreground tools, resources, and prompts can inject them too, not only task-enabled components. `Progress()` can be injected anywhere regardless, though cross-process task progress requires Docket. For comprehensive task patterns, see the [Docket documentation](https://chrisguidry.github.io/docket/dependencies/). </Note>

Custom Dependencies

Beyond the built-in dependencies, you can create your own to inject configuration, database connections, API clients, or any other values your functions need.

Using Depends()

The Depends() function wraps any callable and injects its return value. This works with synchronous functions, async functions, and async context managers.

python

from fastmcp import FastMCP
from fastmcp.dependencies import Depends

mcp = FastMCP("Custom Deps Demo")


def get_config() -> dict:
    return {"api_url": "https://api.example.com", "timeout": 30}


async def get_user_id() -> int:
    # Could fetch from database, external service, etc.
    return 42


@mcp.tool
async def fetch_data(
    query: str,
    config: dict = Depends(get_config),
    user_id: int = Depends(get_user_id),
) -> str:
    return f"User {user_id} fetching '{query}' from {config['api_url']}"

Caching

Dependencies are cached per-request. If multiple parameters use the same dependency, or if nested dependencies share a common dependency, it's resolved once and the same instance is reused.

python

from fastmcp import FastMCP
from fastmcp.dependencies import Depends

mcp = FastMCP("Caching Demo")


def get_db_connection():
    print("Connecting to database...")  # Only printed once per request
    return {"connection": "active"}


def get_user_repo(db=Depends(get_db_connection)):
    return {"db": db, "type": "user"}


def get_order_repo(db=Depends(get_db_connection)):
    return {"db": db, "type": "order"}


@mcp.tool
async def process_order(
    order_id: str,
    users=Depends(get_user_repo),
    orders=Depends(get_order_repo),
) -> str:
    # Both repos share the same db connection
    return f"Processed order {order_id}"

Resource Management

For dependencies that need cleanup—database connections, file handles, HTTP clients—use an async context manager. The cleanup code runs after your function completes, even if an error occurs.

python

from contextlib import asynccontextmanager

from fastmcp import FastMCP
from fastmcp.dependencies import Depends

mcp = FastMCP("Resource Demo")


@asynccontextmanager
async def get_database():
    db = await connect_to_database()
    try:
        yield db
    finally:
        await db.close()


@mcp.tool
async def query_users(sql: str, db=Depends(get_database)) -> list:
    return await db.execute(sql)

Nested Dependencies

Dependencies can depend on other dependencies. FastMCP resolves them in the correct order and applies caching across the dependency tree.

python

from fastmcp import FastMCP
from fastmcp.dependencies import Depends

mcp = FastMCP("Nested Demo")


def get_base_url() -> str:
    return "https://api.example.com"


def get_api_client(base_url: str = Depends(get_base_url)) -> dict:
    return {"base_url": base_url, "version": "v1"}


@mcp.tool
async def call_api(endpoint: str, client: dict = Depends(get_api_client)) -> str:
    return f"Calling {client['base_url']}/{client['version']}/{endpoint}"

Call Arguments

A dependency factory can read the arguments of the function it serves. Declare the reference with CallArgument():

python

from fastmcp import FastMCP
from fastmcp.dependencies import CallArgument, Depends

mcp = FastMCP("Call Arguments Demo")


def get_account(user_id: str = CallArgument()) -> dict:
    return {"id": user_id, "plan": "pro"}


@mcp.tool
async def show_account(user_id: str, account: dict = Depends(get_account)) -> str:
    return f"{account['id']} is on {account['plan']}"

When a client calls show_account, the factory receives the same user_id value the tool receives. The bare form takes the name of the parameter it is declared on. CallArgument("user_id") names the parameter explicitly. The reference also sees a value that another dependency on the tool's signature produced. CallArgument("tenant", optional=True) yields None when the function has no such parameter. References that form a cycle raise CycleError, importable from fastmcp.dependencies.

Clients still cannot override dependencies this way: an argument whose name collides with a dependency parameter is stripped before resolution, so a CallArgument reference to that parameter resolves the dependency itself.

Bindings

Depends() accepts keyword bindings, so you can wire up a factory without changing it:

python

from fastmcp import FastMCP
from fastmcp.dependencies import CallArgument, Depends

mcp = FastMCP("Bindings Demo")


def get_account(user_id: str) -> dict:
    return {"id": user_id, "plan": "pro"}


@mcp.tool
async def show_account(
    owner: str,
    account: dict = Depends(get_account, user_id=CallArgument("owner")),
) -> str:
    return f"{account['id']} is on {account['plan']}"

A binding that is a Dependency, such as CallArgument(...) or another Depends(...), resolves first and the factory receives its value. Any other value passes through as it is. A binding replaces the default of the factory's own parameter, which is then never resolved. Two dependencies on the same factory share one cached result only when their bindings match. See the Docket dependency documentation for more detail on call arguments and bindings.

For advanced dependency patterns—like TaskArgument() for accessing task parameters, or custom Dependency subclasses—see the Docket dependency documentation.