---
title: dependencies
sidebarTitle: dependencies
---
# `fastmcp.server.dependencies`
Dependency injection for FastMCP.
DI features (Depends, CurrentContext, CurrentFastMCP) work without pydocket
using a vendored DI engine. Only task-related dependencies (CurrentDocket,
CurrentWorker) and background task execution require fastmcp[tasks].
## Functions
### `is_docket_available`
```python
is_docket_available() -> bool
```
Check if pydocket is installed.
### `require_docket`
```python
require_docket(feature: str) -> None
```
Raise ImportError with install instructions if docket not available.
**Args:**
- `feature`: Description of what requires docket (e.g., "`task=True`",
"CurrentDocket()"). Will be included in the error message.
### `transform_context_annotations`
```python
transform_context_annotations(fn: Callable[..., Any]) -> Callable[..., Any]
```
Transform ctx: Context into ctx: Context = CurrentContext().
Transforms ALL params typed as Context to use Docket's DI system,
unless they already have a Dependency-based default (like CurrentContext()).
This unifies the legacy type annotation DI with Docket's Depends() system,
allowing both patterns to work through a single resolution path.
Note: Only POSITIONAL_OR_KEYWORD parameters are reordered (params with defaults
after those without). KEYWORD_ONLY parameters keep their position since Python
allows them to have defaults in any order.
**Args:**
- `fn`: Function to transform
**Returns:**
- Function with modified signature (same function object, updated __signature__)
### `get_context`
```python
get_context() -> Context
```
Get the current FastMCP Context instance directly.
### `get_server`
```python
get_server() -> FastMCP
```
Get the current FastMCP server instance directly.
**Returns:**
- The active FastMCP server
**Raises:**
- `RuntimeError`: If no server in context
### `get_http_request`
```python
get_http_request() -> Request
```
Get the current HTTP request.
Tries MCP SDK's request_ctx first, then falls back to FastMCP's HTTP context.
### `get_http_headers`
```python
get_http_headers(include_all: bool = False) -> dict[str, str]
```
Extract headers from the current HTTP request if available.
Never raises an exception, even if there is no active HTTP request (in which case
an empty dict is returned).
By default, strips problematic headers like `content-length` that cause issues
if forwarded to downstream clients. If `include_all` is True, all headers are returned.
### `get_access_token`
```python
get_access_token() -> AccessToken | None
```
Get the FastMCP access token from the current context.
This function first tries to get the token from the current HTTP request's scope,
which is more reliable for long-lived connections where the SDK's auth_context_var
may become stale after token refresh. Falls back to the SDK's context var if no
request is available.
**Returns:**
- The access token if an authenticated user is available, None otherwise.
### `without_injected_parameters`
```python
without_injected_parameters(fn: Callable[..., Any]) -> Callable[..., Any]
```
Create a wrapper function without injected parameters.
Returns a wrapper that excludes Context and Docket dependency parameters,
making it safe to use with Pydantic TypeAdapter for schema generation and
validation. The wrapper internally handles all dependency resolution and
Context injection when called.
Handles:
- Legacy Context injection (always works)
- Depends() injection (always works - uses docket or vendored DI engine)
**Args:**
- `fn`: Original function with Context and/or dependencies
**Returns:**
- Async wrapper function without injected parameters
### `resolve_dependencies`
```python
resolve_dependencies(fn: Callable[..., Any], arguments: dict[str, Any]) -> AsyncGenerator[dict[str, Any], None]
```
Resolve dependencies for a FastMCP function.
This function:
1. Filters out any dependency parameter names from user arguments (security)
2. Resolves Depends() parameters via the DI system
The filtering prevents external callers from overriding injected parameters by
providing values for dependency parameter names. This is a security feature.
Note: Context injection is handled via transform_context_annotations() which
converts `ctx: Context` to `ctx: Context = Depends(get_context)` at registration
time, so all injection goes through the unified DI system.
**Args:**
- `fn`: The function to resolve dependencies for
- `arguments`: User arguments (may contain keys that match dependency names,
which will be filtered out)
### `CurrentContext`
```python
CurrentContext() -> Context
```
Get the current FastMCP Context instance.
This dependency provides access to the active FastMCP Context for the
current MCP operation (tool/resource/prompt call).
**Returns:**
- A dependency that resolves to the active Context instance
**Raises:**
- `RuntimeError`: If no active context found (during resolution)
### `CurrentDocket`
```python
CurrentDocket() -> Docket
```
Get the current Docket instance managed by FastMCP.
This dependency provides access to the Docket instance that FastMCP
automatically creates for background task scheduling.
**Returns:**
- A dependency that resolves to the active Docket instance
**Raises:**
- `RuntimeError`: If not within a FastMCP server context
- `ImportError`: If fastmcp[tasks] not installed
### `CurrentWorker`
```python
CurrentWorker() -> Worker
```
Get the current Docket Worker instance managed by FastMCP.
This dependency provides access to the Worker instance that FastMCP
automatically creates for background task processing.
**Returns:**
- A dependency that resolves to the active Worker instance
**Raises:**
- `RuntimeError`: If not within a FastMCP server context
- `ImportError`: If fastmcp[tasks] not installed
### `CurrentFastMCP`
```python
CurrentFastMCP() -> FastMCP
```
Get the current FastMCP server instance.
This dependency provides access to the active FastMCP server.
**Returns:**
- A dependency that resolves to the active FastMCP server
**Raises:**
- `RuntimeError`: If no server in context (during resolution)
## Classes
### `ProgressLike`
Protocol for progress tracking interface.
Defines the common interface between InMemoryProgress (server context)
and Docket's Progress (worker context).
**Methods:**
#### `current`
```python
current(self) -> int | None
```
Current progress value.
#### `total`
```python
total(self) -> int
```
Total/target progress value.
#### `message`
```python
message(self) -> str | None
```
Current progress message.
#### `set_total`
```python
set_total(self, total: int) -> None
```
Set the total/target value for progress tracking.
#### `increment`
```python
increment(self, amount: int = 1) -> None
```
Atomically increment the current progress value.
#### `set_message`
```python
set_message(self, message: str | None) -> None
```
Update the progress status message.
### `InMemoryProgress`
In-memory progress tracker for immediate tool execution.
Provides the same interface as Docket's Progress but stores state in memory
instead of Redis. Useful for testing and immediate execution where
progress doesn't need to be observable across processes.
**Methods:**
#### `current`
```python
current(self) -> int | None
```
#### `total`
```python
total(self) -> int
```
#### `message`
```python
message(self) -> str | None
```
#### `set_total`
```python
set_total(self, total: int) -> None
```
Set the total/target value for progress tracking.
#### `increment`
```python
increment(self, amount: int = 1) -> None
```
Atomically increment the current progress value.
#### `set_message`
```python
set_message(self, message: str | None) -> None
```
Update the progress status message.
### `Progress`
FastMCP Progress dependency that works in both server and worker contexts.
Handles three execution modes:
- In Docket worker: Uses the execution's progress (observable via Redis)
- In FastMCP server with Docket: Falls back to in-memory progress
- In FastMCP server without Docket: Uses in-memory progress
This allows tools to use Progress() regardless of whether they're called
immediately or as background tasks, and regardless of whether pydocket
is installed.