"""FastMCP - A more ergonomic interface for MCP servers.""" import json from collections.abc import AsyncIterator, Awaitable, Callable from contextlib import ( AbstractAsyncContextManager, AsyncExitStack, asynccontextmanager, ) from typing import TYPE_CHECKING, Any, Generic, Literal import anyio import httpx import pydantic_core import uvicorn from fastapi import FastAPI from mcp.server.lowlevel.helper_types import ReadResourceContents from mcp.server.lowlevel.server import LifespanResultT from mcp.server.lowlevel.server import Server as MCPServer from mcp.server.session import ServerSession from mcp.server.sse import SseServerTransport from mcp.server.stdio import stdio_server from mcp.types import ( AnyFunction, EmbeddedResource, GetPromptResult, ImageContent, TextContent, ) from mcp.types import Prompt as MCPPrompt from mcp.types import PromptArgument as MCPPromptArgument from mcp.types import Resource as MCPResource from mcp.types import ResourceTemplate as MCPResourceTemplate from mcp.types import Tool as MCPTool from pydantic.networks import AnyUrl from starlette.applications import Starlette from starlette.requests import Request from starlette.routing import Mount, Route import fastmcp import fastmcp.settings from fastmcp.exceptions import ResourceError from fastmcp.prompts import Prompt, PromptManager from fastmcp.prompts.prompt import Message, PromptResult from fastmcp.resources import Resource, ResourceManager from fastmcp.resources.template import ResourceTemplate from fastmcp.tools import ToolManager from fastmcp.tools.tool import Tool from fastmcp.utilities.decorators import DecoratedFunction from fastmcp.utilities.logging import configure_logging, get_logger from fastmcp.utilities.types import Image if TYPE_CHECKING: from fastmcp.client import Client from fastmcp.server.context import Context from fastmcp.server.openapi import FastMCPOpenAPI from fastmcp.server.proxy import FastMCPProxy logger = get_logger(__name__) @asynccontextmanager async def default_lifespan(server: "FastMCP") -> AsyncIterator[Any]: """Default lifespan context manager that does nothing. Args: server: The server instance this lifespan is managing Returns: An empty context object """ yield {} def lifespan_wrapper( app: "FastMCP", lifespan: Callable[["FastMCP"], AbstractAsyncContextManager[LifespanResultT]], ) -> Callable[ [MCPServer[LifespanResultT]], AbstractAsyncContextManager[LifespanResultT] ]: @asynccontextmanager async def wrap(s: MCPServer[LifespanResultT]) -> AsyncIterator[LifespanResultT]: async with AsyncExitStack() as stack: # enter main app's lifespan context = await stack.enter_async_context(lifespan(app)) # Enter all mounted app lifespans for prefix, mounted_app in app._mounted_apps.items(): mounted_context = mounted_app._mcp_server.lifespan( mounted_app._mcp_server ) await stack.enter_async_context(mounted_context) logger.debug(f"Prepared lifespan for mounted app '{prefix}'") yield context return wrap class FastMCP(Generic[LifespanResultT]): def __init__( self, name: str | None = None, instructions: str | None = None, lifespan: ( Callable[["FastMCP"], AbstractAsyncContextManager[LifespanResultT]] | None ) = None, tags: set[str] | None = None, **settings: Any, ): self.tags: set[str] = tags or set() self.settings = fastmcp.settings.ServerSettings(**settings) # Setup for mounted apps - must be initialized before _mcp_server self._mounted_apps: dict[str, FastMCP] = {} if lifespan is None: lifespan = default_lifespan self._mcp_server = MCPServer[LifespanResultT]( name=name or "FastMCP", instructions=instructions, lifespan=lifespan_wrapper(self, lifespan), ) self._tool_manager = ToolManager( duplicate_behavior=self.settings.on_duplicate_tools ) self._resource_manager = ResourceManager( duplicate_behavior=self.settings.on_duplicate_resources ) self._prompt_manager = PromptManager( duplicate_behavior=self.settings.on_duplicate_prompts ) self.dependencies = self.settings.dependencies # Set up MCP protocol handlers self._setup_handlers() # Configure logging configure_logging(self.settings.log_level) @property def name(self) -> str: return self._mcp_server.name @property def instructions(self) -> str | None: return self._mcp_server.instructions async def run_async( self, transport: Literal["stdio", "sse"] | None = None, **transport_kwargs: Any ) -> None: """Run the FastMCP server asynchronously. Args: transport: Transport protocol to use ("stdio" or "sse") """ if transport is None: transport = "stdio" if transport not in ["stdio", "sse"]: raise ValueError(f"Unknown transport: {transport}") if transport == "stdio": await self.run_stdio_async(**transport_kwargs) else: # transport == "sse" await self.run_sse_async(**transport_kwargs) def run( self, transport: Literal["stdio", "sse"] | None = None, **transport_kwargs: Any ) -> None: """Run the FastMCP server. Note this is a synchronous function. Args: transport: Transport protocol to use ("stdio" or "sse") """ logger.info(f'Starting server "{self.name}"...') anyio.run(self.run_async, transport, **transport_kwargs) def _setup_handlers(self) -> None: """Set up core MCP protocol handlers.""" self._mcp_server.list_tools()(self._mcp_list_tools) self._mcp_server.call_tool()(self.call_tool) self._mcp_server.list_resources()(self._mcp_list_resources) self._mcp_server.read_resource()(self._mcp_read_resource) self._mcp_server.list_prompts()(self._mcp_list_prompts) self._mcp_server.get_prompt()(self._mcp_get_prompt) self._mcp_server.list_resource_templates()(self._mcp_list_resource_templates) def list_tools(self) -> list[Tool]: return self._tool_manager.list_tools() async def _mcp_list_tools(self) -> list[MCPTool]: """ List all available tools, in the format expected by the low-level MCP server. See `list_tools` for a more ergonomic way to list tools. """ tools = self.list_tools() return [ MCPTool( name=info.name, description=info.description, inputSchema=info.parameters, ) for info in tools ] def get_context(self) -> "Context[ServerSession, LifespanResultT]": """ Returns a Context object. Note that the context will only be valid during a request; outside a request, most methods will error. """ try: request_context = self._mcp_server.request_context except LookupError: request_context = None from fastmcp.server.context import Context return Context(request_context=request_context, fastmcp=self) async def call_tool( self, name: str, arguments: dict[str, Any] ) -> list[TextContent | ImageContent | EmbeddedResource]: """Call a tool by name with arguments.""" context = self.get_context() result = await self._tool_manager.call_tool(name, arguments, context=context) converted_result = _convert_to_content(result) return converted_result def list_resources(self) -> list[Resource]: return self._resource_manager.list_resources() async def _mcp_list_resources(self) -> list[MCPResource]: """ List all available resources, in the format expected by the low-level MCP server. See `list_resources` for a more ergonomic way to list resources. """ resources = self.list_resources() return [ MCPResource( uri=resource.uri, name=resource.name or "", description=resource.description, mimeType=resource.mime_type, ) for resource in resources ] def list_resource_templates(self) -> list[ResourceTemplate]: return self._resource_manager.list_templates() async def _mcp_list_resource_templates(self) -> list[MCPResourceTemplate]: """ List all available resource templates, in the format expected by the low-level MCP server. See `list_resource_templates` for a more ergonomic way to list resource templates. """ templates = self.list_resource_templates() return [ MCPResourceTemplate( uriTemplate=template.uri_template, name=template.name, description=template.description, ) for template in templates ] async def read_resource(self, uri: AnyUrl | str) -> str | bytes: """Read a resource by URI.""" resource = await self._resource_manager.get_resource(uri) if not resource: raise ResourceError(f"Unknown resource: {uri}") return await resource.read() async def _mcp_read_resource(self, uri: AnyUrl | str) -> list[ReadResourceContents]: """ Read a resource by URI, in the format expected by the low-level MCP server. See `read_resource` for a more ergonomic way to read resources. """ resource = await self._resource_manager.get_resource(uri) if not resource: raise ResourceError(f"Unknown resource: {uri}") try: content = await self.read_resource(uri) return [ReadResourceContents(content=content, mime_type=resource.mime_type)] except Exception as e: logger.error(f"Error reading resource {uri}: {e}") raise ResourceError(str(e)) def add_tool( self, fn: AnyFunction, name: str | None = None, description: str | None = None, tags: set[str] | None = None, ) -> None: """Add a tool to the server. The tool function can optionally request a Context object by adding a parameter with the Context type annotation. See the @tool decorator for examples. Args: fn: The function to register as a tool name: Optional name for the tool (defaults to function name) description: Optional description of what the tool does tags: Optional set of tags for categorizing the tool """ self._tool_manager.add_tool_from_fn( fn, name=name, description=description, tags=tags ) def tool( self, name: str | None = None, description: str | None = None, tags: set[str] | None = None, ) -> Callable[[AnyFunction], AnyFunction]: """Decorator to register a tool. Tools can optionally request a Context object by adding a parameter with the Context type annotation. The context provides access to MCP capabilities like logging, progress reporting, and resource access. Args: name: Optional name for the tool (defaults to function name) description: Optional description of what the tool does tags: Optional set of tags for categorizing the tool Example: @server.tool() def my_tool(x: int) -> str: return str(x) @server.tool() def tool_with_context(x: int, ctx: Context) -> str: ctx.info(f"Processing {x}") return str(x) @server.tool() async def async_tool(x: int, context: Context) -> str: await context.report_progress(50, 100) return str(x) """ # Check if user passed function directly instead of calling decorator if callable(name): raise TypeError( "The @tool decorator was used incorrectly. " "Did you forget to call it? Use @tool() instead of @tool" ) def decorator(fn: AnyFunction) -> AnyFunction: self.add_tool(fn, name=name, description=description, tags=tags) return DecoratedFunction(fn) return decorator def add_resource(self, resource: Resource) -> None: """Add a resource to the server. Args: resource: A Resource instance to add """ self._resource_manager.add_resource(resource) def add_resource_fn( self, fn: AnyFunction, uri: str, name: str | None = None, description: str | None = None, mime_type: str | None = None, tags: set[str] | None = None, ) -> None: """Add a resource or template to the server from a function. If the URI contains parameters (e.g. "resource://{param}") or the function has parameters, it will be registered as a template resource. Args: fn: The function to register as a resource uri: The URI for the resource name: Optional name for the resource description: Optional description of the resource mime_type: Optional MIME type for the resource tags: Optional set of tags for categorizing the resource """ self._resource_manager.add_resource_or_template_from_fn( fn=fn, uri=uri, name=name, description=description, mime_type=mime_type, tags=tags, ) def resource( self, uri: str, *, name: str | None = None, description: str | None = None, mime_type: str | None = None, tags: set[str] | None = None, ) -> Callable[[AnyFunction], AnyFunction]: """Decorator to register a function as a resource. The function will be called when the resource is read to generate its content. The function can return: - str for text content - bytes for binary content - other types will be converted to JSON If the URI contains parameters (e.g. "resource://{param}") or the function has parameters, it will be registered as a template resource. Args: uri: URI for the resource (e.g. "resource://my-resource" or "resource://{param}") name: Optional name for the resource description: Optional description of the resource mime_type: Optional MIME type for the resource tags: Optional set of tags for categorizing the resource Example: @server.resource("resource://my-resource") def get_data() -> str: return "Hello, world!" @server.resource("resource://my-resource") async get_data() -> str: data = await fetch_data() return f"Hello, world! {data}" @server.resource("resource://{city}/weather") def get_weather(city: str) -> str: return f"Weather for {city}" @server.resource("resource://{city}/weather") async def get_weather(city: str) -> str: data = await fetch_weather(city) return f"Weather for {city}: {data}" """ # Check if user passed function directly instead of calling decorator if callable(uri): raise TypeError( "The @resource decorator was used incorrectly. " "Did you forget to call it? Use @resource('uri') instead of @resource" ) def decorator(fn: AnyFunction) -> AnyFunction: self._resource_manager.add_resource_or_template_from_fn( fn=fn, uri=uri, name=name, description=description, mime_type=mime_type, tags=tags, ) return DecoratedFunction(fn) return decorator def add_prompt( self, fn: Callable[..., PromptResult | Awaitable[PromptResult]], name: str | None = None, description: str | None = None, tags: set[str] | None = None, ) -> None: """Add a prompt to the server. Args: prompt: A Prompt instance to add """ self._prompt_manager.add_prompt_from_fn( fn=fn, name=name, description=description, tags=tags, ) def prompt( self, name: str | None = None, description: str | None = None, tags: set[str] | None = None, ) -> Callable[[AnyFunction], AnyFunction]: """Decorator to register a prompt. Args: name: Optional name for the prompt (defaults to function name) description: Optional description of what the prompt does tags: Optional set of tags for categorizing the prompt Example: @server.prompt() def analyze_table(table_name: str) -> list[Message]: schema = read_table_schema(table_name) return [ { "role": "user", "content": f"Analyze this schema:\n{schema}" } ] @server.prompt() async def analyze_file(path: str) -> list[Message]: content = await read_file(path) return [ { "role": "user", "content": { "type": "resource", "resource": { "uri": f"file://{path}", "text": content } } } ] """ # Check if user passed function directly instead of calling decorator if callable(name): raise TypeError( "The @prompt decorator was used incorrectly. " "Did you forget to call it? Use @prompt() instead of @prompt" ) def decorator(func: AnyFunction) -> AnyFunction: self.add_prompt(func, name=name, description=description, tags=tags) return DecoratedFunction(func) return decorator async def run_stdio_async(self) -> None: """Run the server using stdio transport.""" async with stdio_server() as (read_stream, write_stream): await self._mcp_server.run( read_stream, write_stream, self._mcp_server.create_initialization_options(), ) async def run_sse_async( self, host: str | None = None, port: int | None = None, log_level: str | None = None, ) -> None: """Run the server using SSE transport.""" starlette_app = self.sse_app() config = uvicorn.Config( starlette_app, host=host or self.settings.host, port=port or self.settings.port, log_level=log_level or self.settings.log_level.lower(), ) server = uvicorn.Server(config) await server.serve() def sse_app(self) -> Starlette: """Return an instance of the SSE server app.""" sse = SseServerTransport(self.settings.message_path) async def handle_sse(request: Request) -> None: async with sse.connect_sse( request.scope, request.receive, request._send, # type: ignore[reportPrivateUsage] ) as streams: await self._mcp_server.run( streams[0], streams[1], self._mcp_server.create_initialization_options(), ) return Starlette( debug=self.settings.debug, routes=[ Route(self.settings.sse_path, endpoint=handle_sse), Mount(self.settings.message_path, app=sse.handle_post_message), ], ) def list_prompts(self) -> list[Prompt]: """ List all available prompts. """ return self._prompt_manager.list_prompts() async def _mcp_list_prompts(self) -> list[MCPPrompt]: """ List all available prompts, in the format expected by the low-level MCP server. See `list_prompts` for a more ergonomic way to list prompts. """ prompts = self.list_prompts() return [ MCPPrompt( name=prompt.name, description=prompt.description, arguments=[ MCPPromptArgument( name=arg.name, description=arg.description, required=arg.required, ) for arg in (prompt.arguments or []) ], ) for prompt in prompts ] async def get_prompt( self, name: str, arguments: dict[str, Any] | None = None ) -> list[Message]: """Get a prompt by name with arguments.""" return await self._prompt_manager.render_prompt(name, arguments) async def _mcp_get_prompt( self, name: str, arguments: dict[str, Any] | None = None ) -> GetPromptResult: """ Get a prompt by name with arguments, in the format expected by the low-level MCP server. See `get_prompt` for a more ergonomic way to get prompts. """ try: messages = await self.get_prompt(name, arguments) return GetPromptResult(messages=pydantic_core.to_jsonable_python(messages)) except Exception as e: logger.error(f"Error getting prompt {name}: {e}") raise ValueError(str(e)) def mount( self, prefix: str, app: "FastMCP", tool_separator: str | None = None, resource_separator: str | None = None, prompt_separator: str | None = None, ) -> None: """Mount another FastMCP application with a given prefix. When an application is mounted: - The tools are imported with prefixed names using the tool_separator Example: If app has a tool named "get_weather", it will be available as "weatherget_weather" - The resources are imported with prefixed URIs using the resource_separator Example: If app has a resource with URI "weather://forecast", it will be available as "weather+weather://forecast" - The templates are imported with prefixed URI templates using the resource_separator Example: If app has a template with URI "weather://location/{id}", it will be available as "weather+weather://location/{id}" - The prompts are imported with prefixed names using the prompt_separator Example: If app has a prompt named "weather_prompt", it will be available as "weather_weather_prompt" - The mounted app's lifespan will be executed when the parent app's lifespan runs, ensuring that any setup needed by the mounted app is performed Args: prefix: The prefix to use for the mounted application app: The FastMCP application to mount tool_separator: Separator for tool names (defaults to "_") resource_separator: Separator for resource URIs (defaults to "+") prompt_separator: Separator for prompt names (defaults to "_") """ if tool_separator is None: tool_separator = "_" if resource_separator is None: resource_separator = "+" if prompt_separator is None: prompt_separator = "_" # Mount the app in the list of mounted apps self._mounted_apps[prefix] = app # Import tools from the mounted app tool_prefix = f"{prefix}{tool_separator}" self._tool_manager.import_tools(app._tool_manager, tool_prefix) # Import resources and templates from the mounted app resource_prefix = f"{prefix}{resource_separator}" self._resource_manager.import_resources(app._resource_manager, resource_prefix) self._resource_manager.import_templates(app._resource_manager, resource_prefix) # Import prompts from the mounted app prompt_prefix = f"{prefix}{prompt_separator}" self._prompt_manager.import_prompts(app._prompt_manager, prompt_prefix) logger.info(f"Mounted app with prefix '{prefix}'") logger.debug(f"Imported tools with prefix '{tool_prefix}'") logger.debug(f"Imported resources with prefix '{resource_prefix}'") logger.debug(f"Imported templates with prefix '{resource_prefix}'") logger.debug(f"Imported prompts with prefix '{prompt_prefix}'") @classmethod async def as_proxy( cls, client: "Client | FastMCP", **settings: Any ) -> "FastMCPProxy": """ Create a FastMCP proxy server from a client. This method creates a new FastMCP server instance that proxies requests to the provided client. It discovers the client's tools, resources, prompts, and templates, and creates corresponding components in the server that forward requests to the client. Args: client: The client to proxy requests to **settings: Additional settings for the FastMCP server Returns: A FastMCP server that proxies requests to the client """ from fastmcp.client import Client from .proxy import FastMCPProxy if isinstance(client, Client): return await FastMCPProxy.from_client(client=client, **settings) elif isinstance(client, FastMCP): return await FastMCPProxy.from_server(server=client, **settings) else: raise ValueError(f"Unknown client type: {type(client)}") @classmethod def from_openapi( cls, openapi_spec: dict[str, Any], client: httpx.AsyncClient, **settings: Any ) -> "FastMCPOpenAPI": """ Create a FastMCP server from an OpenAPI specification. """ from .openapi import FastMCPOpenAPI return FastMCPOpenAPI(openapi_spec=openapi_spec, client=client, **settings) @classmethod def from_fastapi( cls, app: FastAPI, name: str | None = None, **settings: Any ) -> "FastMCPOpenAPI": """ Create a FastMCP server from a FastAPI application. """ from .openapi import FastMCPOpenAPI client = httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="http://fastapi" ) name = name or app.title return FastMCPOpenAPI( openapi_spec=app.openapi(), client=client, name=name, **settings ) def _convert_to_content( result: Any, _process_as_single_item: bool = False, ) -> list[TextContent | ImageContent | EmbeddedResource]: """Convert a result to a sequence of content objects.""" if result is None: return [] if isinstance(result, TextContent | ImageContent | EmbeddedResource): return [result] if isinstance(result, Image): return [result.to_image_content()] if isinstance(result, list | tuple) and not _process_as_single_item: # if the result is a list, then it could either be a list of MCP types, # or a "regular" list that the tool is returning, or a mix of both. # # so we extract all the MCP types / images and convert them as individual content elements, # and aggregate the rest as a single content element mcp_types = [] other_content = [] for item in result: if isinstance(item, TextContent | ImageContent | EmbeddedResource | Image): mcp_types.append(_convert_to_content(item)[0]) else: other_content.append(item) if other_content: other_content = _convert_to_content( other_content, _process_as_single_item=True ) return other_content + mcp_types if not isinstance(result, str): try: result = json.dumps(pydantic_core.to_jsonable_python(result)) except Exception: result = str(result) return [TextContent(type="text", text=result)]