Access MCP capabilities like logging, progress, and resources within your MCP objects.
When defining FastMCP tools, resources, resource templates, or prompts, your functions might need to interact with the underlying MCP session or access advanced server capabilities. FastMCP provides the Context object for this purpose.
FastMCP uses Docket’s dependency injection system for managing runtime dependencies. This page covers Context and the built-in dependencies; see Custom Dependencies for creating your own.
Dependency parameters are automatically excluded from the MCP schema—clients never see them.
Context methods are async, so your function usually needs to be async as well.
Each MCP request receives a new context object. Context is scoped to a single request; state or data set in one request will not be available in subsequent requests.
Context is only available during a request; attempting to use context methods outside a request will raise errors.
For backwards compatibility, you can still access context by simply adding a parameter with the Context type hint. FastMCP will automatically inject the context instance:
Copy
from fastmcp import FastMCP, Contextmcp = FastMCP(name="Context Demo")@mcp.toolasync def process_file(file_uri: str, ctx: Context) -> str: """Processes a file, using context for logging and resource access.""" # Context is injected automatically based on the type hint return "Processed file"
This approach still works for tools, resources, and prompts. The parameter name doesn’t matter—only the Context type hint is important. The type hint can also be a union (Context | None) or use Annotated[].
New in version 2.2.11For code nested deeper within your function calls where passing context through parameters is inconvenient, use get_context() to retrieve the active context from anywhere within a request’s execution flow:
Copy
from fastmcp import FastMCPfrom fastmcp.server.dependencies import get_contextmcp = FastMCP(name="Dependency Demo")# Utility function that needs context but doesn't receive it as a parameterasync def process_data(data: list[float]) -> dict: # Get the active context - only works when called within a request ctx = get_context() await ctx.info(f"Processing {len(data)} data points")@mcp.toolasync def analyze_dataset(dataset_name: str) -> dict: # Call utility function that uses context internally data = load_data(dataset_name) await process_data(data)
Important Notes:
The get_context() function should only be used within the context of a server request. Calling it outside of a request will raise a RuntimeError.
The get_context() function is server-only and should not be used in client code.
FastMCP provides several advanced capabilities through the context object. Each capability has dedicated documentation with comprehensive examples and best practices:
New in version 2.10.0Request structured input from clients during tool execution, enabling interactive workflows and progressive disclosure. This is a new feature in the 6/18/2025 MCP spec.
Copy
result = await ctx.elicit("Enter your name:", response_type=str)if result.action == "accept": name = result.data
See User Elicitation for detailed examples and supported response types.
List and read data from resources registered with your FastMCP server, allowing access to files, configuration, or dynamic content.
Copy
# List available resourcesresources = await ctx.list_resources()# Read a specific resourcecontent_list = await ctx.read_resource("resource://config")content = content_list[0].content
Method signatures:
ctx.list_resources() -> list[MCPResource]: New in version 2.13.0 Returns list of all available resources
ctx.read_resource(uri: str | AnyUrl) -> list[ReadResourceContents]: Returns a list of resource content parts
New in version 2.13.0List and retrieve prompts registered with your FastMCP server, allowing tools and middleware to discover and use available prompts programmatically.
Copy
# List available promptsprompts = await ctx.list_prompts()# Get a specific prompt with argumentsresult = await ctx.get_prompt("analyze_data", {"dataset": "users"})messages = result.messages
Method signatures:
ctx.list_prompts() -> list[MCPPrompt]: Returns list of all available prompts
ctx.get_prompt(name: str, arguments: dict[str, Any] | None = None) -> GetPromptResult: Get a specific prompt with optional arguments
New in version 2.11.0Store and share data between middleware and handlers within a single MCP request. Each MCP request (such as calling a tool, reading a resource, listing tools, or listing resources) receives its own context object with isolated state. Context state is particularly useful for passing information from middleware to your handlers.To store a value in the context state, use ctx.set_state(key, value). To retrieve a value, use ctx.get_state(key).
Context state is scoped to a single MCP request. Each operation (tool call, resource read, list operation, etc.) receives a new context object. State set during one request will not be available in subsequent requests. For persistent data storage across requests, use external storage mechanisms like databases, files, or in-memory caches.
This simplified example shows how to use MCP middleware to store user info in the context state, and how to access that state in a tool:
Copy
from fastmcp.server.middleware import Middleware, MiddlewareContextclass UserAuthMiddleware(Middleware): async def on_call_tool(self, context: MiddlewareContext, call_next): # Middleware stores user info in context state context.fastmcp_context.set_state("user_id", "user_123") context.fastmcp_context.set_state("permissions", ["read", "write"]) return await call_next(context)@mcp.toolasync def secure_operation(data: str, ctx: Context) -> str: """Tool can access state set by middleware.""" user_id = ctx.get_state("user_id") # "user_123" permissions = ctx.get_state("permissions") # ["read", "write"] if "write" not in permissions: return "Access denied" return f"Processing {data} for user {user_id}"
Method signatures:
ctx.set_state(key: str, value: Any) -> None: Store a value in the context state
ctx.get_state(key: str) -> Any: Retrieve a value from the context state (returns None if not found)
State Inheritance:
When a new context is created (nested contexts), it inherits a copy of its parent’s state. This ensures that:
State set on a child context never affects the parent context
State set on a parent context after the child context is initialized is not propagated to the child context
This makes state management predictable and prevents unexpected side effects between nested operations.
New in version 2.9.1FastMCP automatically sends list change notifications when components (such as tools, resources, or prompts) are added, removed, enabled, or disabled. In rare cases where you need to manually trigger these notifications, you can use the context methods:
Copy
@mcp.toolasync def custom_tool_management(ctx: Context) -> str: """Example of manual notification after custom tool changes.""" # After making custom changes to tools await ctx.send_tool_list_changed() await ctx.send_resource_list_changed() await ctx.send_prompt_list_changed() return "Notifications sent"
These methods are primarily used internally by FastMCP’s automatic notification system and most users will not need to invoke them directly.
Access metadata about the current request and client.
Copy
@mcp.toolasync def request_info(ctx: Context) -> dict: """Return information about the current request.""" return { "request_id": ctx.request_id, "client_id": ctx.client_id or "Unknown client" }
Available Properties:
ctx.request_id -> str: Get the unique ID for the current MCP request
ctx.client_id -> str | None: Get the ID of the client making the request, if provided during initialization
ctx.session_id -> str | None: Get the MCP session ID for session-based data sharing (HTTP transports only)
New in version 2.13.1The ctx.request_context property provides access to the underlying MCP request context, but returns None when the MCP session has not been established yet. This typically occurs:
During middleware execution in the on_request hook before the MCP handshake completes
During the initialization phase of client connections
The MCP request context is distinct from the HTTP request. For HTTP transports, HTTP request data may be available even when the MCP session is not yet established.To safely access the request context in situations where it may not be available:
Copy
from fastmcp import FastMCP, Contextfrom fastmcp.server.dependencies import get_http_requestmcp = FastMCP(name="Session Aware Demo")@mcp.toolasync def session_info(ctx: Context) -> dict: """Return session information when available.""" # Check if MCP session is available if ctx.request_context: # MCP session available - can access MCP-specific attributes return { "session_id": ctx.session_id, "request_id": ctx.request_id, "has_meta": ctx.request_context.meta is not None } else: # MCP session not available - use HTTP helpers for request data (if using HTTP transport) request = get_http_request() return { "message": "MCP session not available", "user_agent": request.headers.get("user-agent", "Unknown") }
For HTTP request access that works regardless of MCP session availability (when using HTTP transports), use the HTTP request helpers like get_http_request() and get_http_headers().
New in version 2.13.1Clients can send contextual information with their requests using the meta parameter. This metadata is accessible through ctx.request_context.meta and is available for all MCP operations (tools, resources, prompts).The meta field is None when clients don’t provide metadata. When provided, metadata is accessible via attribute access (e.g., meta.user_id) rather than dictionary access. The structure of metadata is determined by the client making the request.
Copy
@mcp.tooldef send_email(to: str, subject: str, body: str, ctx: Context) -> str: """Send an email, logging metadata about the request.""" # Access client-provided metadata meta = ctx.request_context.meta if meta: # Meta is accessed as an object with attribute access user_id = meta.user_id if hasattr(meta, 'user_id') else None trace_id = meta.trace_id if hasattr(meta, 'trace_id') else None # Use metadata for logging, observability, etc. if trace_id: log_with_trace(f"Sending email for user {user_id}", trace_id) # Send the email... return f"Email sent to {to}"
The MCP request is part of the low-level MCP SDK and intended for advanced use cases. Most users will not need to use it directly.
New in version 2.2.11If you only need request headers and want to avoid potential errors, you can use the get_http_headers() helper:
Copy
from fastmcp import FastMCPfrom fastmcp.server.dependencies import get_http_headersmcp = FastMCP(name="Headers Demo")@mcp.toolasync def safe_header_info() -> dict: """Safely get header information without raising errors.""" # Get headers (returns empty dict if no request context) headers = get_http_headers() # Get authorization header auth_header = headers.get("authorization", "") is_bearer = auth_header.startswith("Bearer ") return { "user_agent": headers.get("user-agent", "Unknown"), "content_type": headers.get("content-type", "Unknown"), "has_auth": bool(auth_header), "auth_type": "Bearer" if is_bearer else "Other" if auth_header else "None", "headers_count": len(headers) }
By default, get_http_headers() excludes problematic headers like host and content-length. To include all headers, use get_http_headers(include_all=True).
New in version 2.11.0When using authentication with your FastMCP server, you can access the authenticated user’s access token information using the get_access_token() dependency function:
Copy
from fastmcp import FastMCPfrom fastmcp.server.dependencies import get_access_token, AccessTokenmcp = FastMCP(name="Auth Token Demo")@mcp.toolasync def get_user_info() -> dict: """Get information about the authenticated user.""" # Get the access token (None if not authenticated) token: AccessToken | None = get_access_token() if token is None: return {"authenticated": False} return { "authenticated": True, "client_id": token.client_id, "scopes": token.scopes, "expires_at": token.expires_at, "token_claims": token.claims, # JWT claims or custom token data }
This is particularly useful when you need to:
Access user identification - Get the client_id or subject from token claims
Check permissions - Verify scopes or custom claims before performing operations
Multi-tenant applications - Extract tenant information from token claims
Audit logging - Track which user performed which actions
The claims field contains all the data from the original token (JWT claims for JWT tokens, or custom data for other token types):
Copy
from fastmcp import FastMCPfrom fastmcp.server.dependencies import get_access_tokenmcp = FastMCP(name="Multi-tenant Demo")@mcp.toolasync def get_tenant_data(resource_id: str) -> dict: """Get tenant-specific data using token claims.""" token: AccessToken | None = get_access_token() # Extract tenant ID from token claims tenant_id = token.claims.get("tenant_id") if token else None # Extract user ID from standard JWT subject claim user_id = token.claims.get("sub") if token else None # Use tenant and user info to authorize and filter data if not tenant_id: raise ValueError("No tenant information in token") return { "resource_id": resource_id, "tenant_id": tenant_id, "user_id": user_id, "data": f"Tenant-specific data for {tenant_id}", }
New in version 2.14FastMCP’s dependency injection is powered by Docket, which provides a flexible system for injecting values into your functions. Beyond the built-in dependencies like CurrentContext(), you can create your own.
The simplest way to create a custom dependency is with Depends(). Pass any callable (sync or async function, or async context manager) and its return value will be injected:
For more complex dependency patterns—like dependencies that need access to Docket’s execution context or require custom lifecycle management—you can subclass Docket’s Dependency class. See the Docket documentation on dependencies for details.