MCP Context

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

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.

What Is Context?

The Context object provides a clean interface to access MCP features within your functions, including:

• Logging: Send debug, info, warning, and error messages back to the client
• Progress Reporting: Update the client on the progress of long-running operations
• Resource Access: List and read data from resources registered with the server
• Prompt Access: List and retrieve prompts registered with the server
• LLM Sampling: Request the client's LLM to generate text based on provided messages
• User Elicitation: Request structured input from users during tool execution
• Request State: Pass values and non-serializable resources between middleware and handlers within a request (for state that persists across requests, see Session State)
• Session Visibility: Control which components are visible to the current session
• Request Information: Access metadata about the current request
• Server Access: When needed, access the underlying FastMCP server instance

Accessing the Context

The preferred way to access context is using the CurrentContext() dependency:

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

mcp = FastMCP(name="Context Demo")

@mcp.tool
async def process_file(file_uri: str, ctx: Context = CurrentContext()) -> str:
    """Processes a file, using context for logging and resource access."""
    await ctx.info(f"Processing {file_uri}")
    return "Processed file"

This works with tools, resources, and prompts:

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

mcp = FastMCP(name="Context Demo")

@mcp.resource("resource://user-data")
async def get_user_data(ctx: Context = CurrentContext()) -> dict:
    await ctx.debug("Fetching user data")
    return {"user_id": "example"}

@mcp.prompt
async def data_analysis_request(dataset: str, ctx: Context = CurrentContext()) -> str:
    return f"Please analyze the following dataset: {dataset}"

Key Points:

• 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. State set with ctx.set_state() is scoped to that request and is not available in subsequent ones. To persist state across requests, use Session State.
• Context is only available during a request; attempting to use context methods outside a request will raise errors.

Legacy Type-Hint Injection

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:

from fastmcp import FastMCP, Context

mcp = FastMCP(name="Context Demo")

@mcp.tool
async 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[].

Via get_context() Function

For 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:

from fastmcp import FastMCP
from fastmcp.server.dependencies import get_context

mcp = FastMCP(name="Dependency Demo")

# Utility function that needs context but doesn't receive it as a parameter
async 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.tool
async 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.

Context Capabilities

FastMCP provides several advanced capabilities through the context object. Each capability has dedicated documentation with comprehensive examples and best practices:

Logging

Send debug, info, warning, and error messages back to the MCP client for visibility into function execution.

await ctx.debug("Starting analysis")
await ctx.info(f"Processing {len(data)} items") 
await ctx.warning("Deprecated parameter used")
await ctx.error("Processing failed")

See Server Logging for complete documentation and examples.

Client Elicitation

Request 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.

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.

LLM Sampling

Request the client's LLM to generate text based on provided messages, useful for leveraging AI capabilities within your tools.

response = await ctx.sample("Analyze this data", temperature=0.7)

See LLM Sampling for comprehensive usage and advanced techniques.

Progress Reporting

Update clients on the progress of long-running operations, enabling progress indicators and better user experience.

await ctx.report_progress(progress=50, total=100)  # 50% complete

See Progress Reporting for detailed patterns and examples.

Resource Access

List and read data from resources registered with your FastMCP server, allowing access to files, configuration, or dynamic content.

# List available resources
resources = await ctx.list_resources()

# Read a specific resource
resource_result = await ctx.read_resource("resource://config")
content = resource_result.contents[0].content

Method signatures:

• ctx.list_resources() -> list[mcp_types.Resource]: <VersionBadge version="2.13.0" /> Returns list of all available resources
• ctx.read_resource(uri: str | AnyUrl) -> ResourceResult: Returns a ResourceResult whose .contents list contains the resource content parts

Prompt Access

List and retrieve prompts registered with your FastMCP server, allowing tools and middleware to discover and use available prompts programmatically.

# List available prompts
prompts = await ctx.list_prompts()

# Get a specific prompt with arguments
result = 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

Request State

Request state carries values within a single request, across the middleware → handler pipeline. A request runs through any middleware you've added and then the handler — separate functions that don't share a stack frame, so a plain local variable can't pass anything between them. ctx.set_state / ctx.get_state is that channel.

The common case is a middleware that resolves something once and every tool reads it, rather than each tool recomputing it:

from fastmcp import FastMCP, Context
from fastmcp.server.middleware import Middleware, MiddlewareContext

mcp = FastMCP("app")


class Enrich(Middleware):
    async def on_call_tool(self, context: MiddlewareContext, call_next):
        await context.fastmcp_context.set_state("caller", "alice")
        return await call_next(context)


mcp.add_middleware(Enrich())


@mcp.tool
async def whoami(ctx: Context) -> str:
    return await ctx.get_state("caller") or "unknown"

The state is scoped to the one request and discarded when it returns. State is also inherited by mounted children, so a value a parent middleware sets is visible to a mounted server's tools within the same request.

Method signatures:

• await ctx.set_state(key, value, *, serializable=True) — store a value
• await ctx.get_state(key) — retrieve a value (returns None if not set)
• await ctx.delete_state(key) — remove a value

Non-serializable resources

The most useful thing request state holds is objects you can't persist — a database connection or an HTTP client that a middleware or the lifespan opens and a handler uses. Pass serializable=False:

@mcp.tool
async def my_tool(ctx: Context) -> str:
    client = SomeHTTPClient(base_url="https://api.example.com")
    await ctx.set_state("client", client, serializable=False)

    client = await ctx.get_state("client")
    return await client.fetch("/data")

A serializable=False value lives on the request context for the current call only. It is inherently request-scoped — a live connection can't be serialized and stored — which is exactly why it belongs here rather than in a persistent store.

Persisting across requests

Request state does not survive from one call to the next. When you need a cart, a conversation, or any state that outlives a single request, use Session State — it stores server-side, keyed by the authenticated user, and works on every protocol era. (On session-based, handshake-era connections, serializable request state also persists across the session, but Session State is the deliberate, cross-era way to do it.)

Session Visibility

Tools can customize which components are visible to their current session using ctx.enable_components(), ctx.disable_components(), and ctx.reset_visibility(). They accept the same filters as the server-level methods, so names={"search"} targets a component by name and tags targets a group. These methods apply visibility rules that affect only the calling session, leaving other sessions unchanged. See Per-Session Visibility for complete documentation, filter criteria, and patterns like namespace activation.

Change Notifications

FastMCP 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's notification methods:

import mcp_types

@mcp.tool
async def custom_tool_management(ctx: Context) -> str:
    """Example of manual notification after custom tool changes."""
    await ctx.send_notification(mcp_types.ToolListChangedNotification())
    await ctx.send_notification(mcp_types.ResourceListChangedNotification())
    await ctx.send_notification(mcp_types.PromptListChangedNotification())
    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.

FastMCP Server

To access the underlying FastMCP server instance, you can use the ctx.fastmcp property:

@mcp.tool
async def my_tool(ctx: Context) -> None:
    # Access the FastMCP server instance
    server_name = ctx.fastmcp.name
    ...

Transport

The ctx.transport property indicates which transport is being used to run the server. This is useful when your tool needs to behave differently depending on whether the server is running over STDIO, SSE, or Streamable HTTP. For example, you might want to return shorter responses over STDIO or adjust timeout behavior based on transport characteristics.

The transport type is set once when the server starts and remains constant for the server's lifetime. It returns None when called outside of a server context (for example, in unit tests or when running code outside of an MCP request).

from fastmcp import FastMCP, Context

mcp = FastMCP("example")

@mcp.tool
def connection_info(ctx: Context) -> str:
    if ctx.transport == "stdio":
        return "Connected via STDIO"
    elif ctx.transport == "sse":
        return "Connected via SSE"
    elif ctx.transport == "streamable-http":
        return "Connected via Streamable HTTP"
    else:
        return "Transport unknown"

Property signature: ctx.transport -> Literal["stdio", "sse", "streamable-http"] | None

MCP Request

Access metadata about the current request and client.

@mcp.tool
async 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: Get the MCP session ID for session-based data sharing. Raises RuntimeError if the MCP session is not yet established.

Request Context Availability

The 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:

from fastmcp import FastMCP, Context
from fastmcp.server.dependencies import get_http_request

mcp = FastMCP(name="Session Aware Demo")

@mcp.tool
async 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().

Client Metadata

Clients 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.

@mcp.tool
def 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}"