Simple HTTP MCP Server Implementation
This project provides a lightweight server implementation for the Model Context
Protocol (MCP) over HTTP. It allows you to expose Python functions as tools and
prompts that can be discovered and executed remotely via a JSON-RPC interface.
It is intended to be used with a Starlette or FastAPI application (see
demo).
Table of Contents
Related MCP server: Python REPL MCP Server
Features
MCP Protocol Compliant: Implements the MCP specification for tool and
prompts discovery and execution. No support for notifications.
HTTP and STDIO Transport: Uses HTTP (POST requests) or STDIO for
communication.
Async Support: Built on Starlette or FastAPI for asynchronous request
handling.
Type-Safe: Leverages Pydantic for robust data validation and
serialization.
Server State Management: Access shared state through the lifespan context
using the get_state_key method.
Request Access: Access the incoming request object from your tools and
prompts.
Authorization Scopes: Support for scope-based authorization using
Starlette's authentication system.
Error Handling: Tools can optionally return error messages instead of
raising exceptions.
Server Architecture
The library provides a single MCPServer class that uses lifespan to manage
shared state across the entire application lifecycle.
MCPServer
The MCPServer is designed to work with Starlette's lifespan system for
managing shared server state.
Key Characteristics:
Lifespan Based: Uses Starlette's lifespan events to initialize and manage
shared server state
Application-Level State: State persists across the entire application
lifecycle, not per-request
Flexible: Can be used with any custom context class stored in the lifespan
state
Constructor Parameters:
name (str): The name of your MCP server
version (str): The version of your MCP server
tools (tuple[Tool, ...]): Tuple of tools to expose (default: empty tuple)
prompts (tuple[Prompt, ...]): Tuple of prompts to expose (default: empty
tuple)
instructions (str | None): Optional instructions for AI assistants on how to
use this server
Example Usage:
import contextlib
from collections.abc import AsyncIterator
from typing import TypedDict
from dataclasses import dataclass, field
from starlette.applications import Starlette
from http_mcp.server import MCPServer
@dataclass
class Context:
call_count: int = 0
user_preferences: dict = field(default_factory=dict)
class State(TypedDict):
context: Context
@contextlib.asynccontextmanager
async def lifespan(_app: Starlette) -> AsyncIterator[State]:
yield {"context": Context()}
mcp_server = MCPServer(
name="my-server",
version="1.0.0",
tools=my_tools,
prompts=my_prompts,
instructions="Optional instructions for AI assistants on how to use this server"
)
app = Starlette(lifespan=lifespan)
app.mount("/mcp", mcp_server.app)
Tools
Tools are the functions that can be called by the client.
Basic Tool Example
Define the arguments and output for the tools:
# app/tools/models.py
from pydantic import BaseModel, Field
class GreetInput(BaseModel):
question: str = Field(description="The question to answer")
class GreetOutput(BaseModel):
answer: str = Field(description="The answer to the question")
# Note: the description on Field will be passed when listing the tools.
# Having a description is optional, but it's recommended to provide one.
Define the tools:
# app/tools/tools.py
from http_mcp.types import Arguments
from app.tools.models import GreetInput, GreetOutput
def greet(args: Arguments[GreetInput]) -> GreetOutput:
return GreetOutput(answer=f"Hello, {args.inputs.question}!")
# app/tools/__init__.py
from http_mcp.types import Tool
from app.tools.models import GreetInput, GreetOutput
from app.tools.tools import greet
TOOLS = (
Tool(
func=greet,
inputs=GreetInput,
output=GreetOutput,
),
)
__all__ = ["TOOLS"]
Instantiate the server:
# app/main.py
from starlette.applications import Starlette
from http_mcp.server import MCPServer
from app.tools import TOOLS
mcp_server = MCPServer(tools=TOOLS, name="test", version="1.0.0")
app = Starlette()
app.mount(
"/mcp",
mcp_server.app,
)
Tools Without Arguments
You can define tools that don't require any input arguments:
from datetime import UTC, datetime
from pydantic import BaseModel, Field
from http_mcp.types import Tool
class GetTimeOutput(BaseModel):
time: str = Field(description="The current time")
async def get_time() -> GetTimeOutput:
"""Get the current time."""
return GetTimeOutput(time=datetime.now(UTC).strftime("%H:%M:%S"))
TOOLS = (
Tool(
func=get_time,
inputs=type(None), # No arguments required
output=GetTimeOutput,
),
)
Alternatively, you can use the NoArguments class for better clarity:
from http_mcp.types import Arguments, NoArguments, Tool
class SimpleOutput(BaseModel):
success: bool = Field(description="Whether the operation was successful")
def simple_tool(args: Arguments[NoArguments]) -> SimpleOutput:
"""A simple tool with no arguments."""
# You can still access request and state
context = args.get_state_key("context", Context)
return SimpleOutput(success=True)
TOOLS = (
Tool(
func=simple_tool,
inputs=NoArguments,
output=SimpleOutput,
),
)
Tools with Error Handling
Tools can optionally return error messages instead of raising exceptions:
from pydantic import BaseModel, Field
from http_mcp.types import Arguments, Tool
from http_mcp.exceptions import ToolInvocationError
class RiskyToolInput(BaseModel):
value: int = Field(description="An integer value")
class RiskyToolOutput(BaseModel):
result: str = Field(description="The result of the operation")
def risky_tool(args: Arguments[RiskyToolInput]) -> RiskyToolOutput:
"""A tool that might fail."""
if args.inputs.value < 0:
raise ToolInvocationError("risky_tool", "Value must be positive")
return RiskyToolOutput(result=f"Success: {args.inputs.value}")
TOOLS = (
Tool(
func=risky_tool,
inputs=RiskyToolInput,
output=RiskyToolOutput,
return_error_message=True, # Return ErrorMessage instead of raising
),
)
When return_error_message=True, the tool will return an ErrorMessage model
with the error details instead of raising a ToolInvocationError.
Tools with Authorization Scopes
You can restrict tool access based on authentication scopes:
from http_mcp.types import Arguments, NoArguments, Tool
from starlette.authentication import has_required_scope
class SecureOutput(BaseModel):
message: str = Field(description="A secure message")
def private_tool(args: Arguments[NoArguments]) -> SecureOutput:
"""A tool that requires authentication."""
assert has_required_scope(args.request, ("private",))
return SecureOutput(message="This is private data")
def admin_tool(args: Arguments[NoArguments]) -> SecureOutput:
"""A tool that requires admin or superuser scope."""
assert has_required_scope(args.request, ("admin", "superuser"))
return SecureOutput(message="This is admin data")
TOOLS = (
Tool(
func=private_tool,
inputs=NoArguments,
output=SecureOutput,
scopes=("private",), # Only accessible with 'private' scope
),
Tool(
func=admin_tool,
inputs=NoArguments,
output=SecureOutput,
scopes=("admin", "superuser"), # Accessible with either scope
),
)
Note: You need to set up authentication middleware in your Starlette app for
scopes to work properly.
Server State Management
The server uses Starlette's lifespan system to manage shared state across the
entire application lifecycle. State is initialized when the application starts
and persists until it shuts down. Context is accessed through the
get_state_key method on the Arguments object.
Example:
Define a context class:
from dataclasses import dataclass, field
# app/context.py
@dataclass
class Context:
called_tools: list[str] = field(default_factory=list)
def get_called_tools(self) -> list[str]:
return self.called_tools
def add_called_tool(self, tool_name: str) -> None:
self.called_tools.append(tool_name)
Set up the application with lifespan:
import contextlib
from collections.abc import AsyncIterator
from typing import TypedDict
from starlette.applications import Starlette
from app.context import Context
from http_mcp.server import MCPServer
class State(TypedDict):
context: Context
@contextlib.asynccontextmanager
async def lifespan(_app: Starlette) -> AsyncIterator[State]:
yield {"context": Context(called_tools=[])}
mcp_server = MCPServer(
tools=TOOLS,
name="test",
version="1.0.0",
)
app = Starlette(lifespan=lifespan)
app.mount("/mcp", mcp_server.app)
Access the context in your tools:
from pydantic import BaseModel, Field
from http_mcp.types import Arguments
from app.context import Context
class MyToolArguments(BaseModel):
question: str = Field(description="The question to answer")
class MyToolOutput(BaseModel):
answer: str = Field(description="The answer to the question")
async def my_tool(args: Arguments[MyToolArguments]) -> MyToolOutput:
# Access the context from lifespan state
context = args.get_state_key("context", Context)
context.add_called_tool("my_tool")
...
return MyToolOutput(answer=f"Hello, {args.inputs.question}!")
Request Access
You can access the incoming request object from your tools. The request object
is passed to each tool call and can be used to access headers, cookies, and
other request data (e.g. request.state, request.scope).
from pydantic import BaseModel, Field
from http_mcp.types import Arguments
class MyToolArguments(BaseModel):
question: str = Field(description="The question to answer")
class MyToolOutput(BaseModel):
answer: str = Field(description="The answer to the question")
async def my_tool(args: Arguments[MyToolArguments]) -> MyToolOutput:
# Access the request
auth_header = args.request.headers.get("Authorization")
...
return MyToolOutput(answer=f"Hello, {args.inputs.question}!")
# Use MCPServer:
from http_mcp.server import MCPServer
mcp_server = MCPServer(
name="my-server",
version="1.0.0",
tools=(my_tool,),
)
Prompts
You can add interactive templates that are invoked by user choice. Prompts now
support lifespan state access, similar to tools.
Basic Prompt Example
Define the arguments for the prompts:
from pydantic import BaseModel, Field
from http_mcp.mcp_types.content import TextContent
from http_mcp.mcp_types.prompts import PromptMessage
from http_mcp.types import Arguments, Prompt
class GetAdvice(BaseModel):
topic: str = Field(description="The topic to get advice on")
include_actionable_steps: bool = Field(
description="Whether to include actionable steps in the advice", default=False
)
def get_advice(args: Arguments[GetAdvice]) -> tuple[PromptMessage, ...]:
"""Get advice on a topic."""
template = """
You are a helpful assistant that can give advice on {topic}.
"""
if args.inputs.include_actionable_steps:
template += """
The advice should include actionable steps.
"""
return (
PromptMessage(
role="user",
content=TextContent(
text=template.format(topic=args.inputs.topic)
),
),
)
PROMPTS = (
Prompt(
func=get_advice,
arguments_type=GetAdvice,
),
)
Instantiate the server:
from starlette.applications import Starlette
from app.prompts import PROMPTS
from http_mcp.server import MCPServer
app = Starlette()
mcp_server = MCPServer(tools=(), prompts=PROMPTS, name="test", version="1.0.0")
app.mount(
"/mcp",
mcp_server.app,
)
Prompts Without Arguments
You can define prompts that don't require any input arguments:
from http_mcp.mcp_types.content import TextContent
from http_mcp.mcp_types.prompts import PromptMessage
from http_mcp.types import Prompt
def help_prompt() -> tuple[PromptMessage, ...]:
"""Use this prompt to get general help."""
return (
PromptMessage(
role="user",
content=TextContent(
text="You are a helpful assistant. Help the user with their task."
),
),
)
PROMPTS = (
Prompt(
func=help_prompt,
arguments_type=type(None), # No arguments required
),
)
Alternatively, you can use the NoArguments class:
from http_mcp.types import Arguments, NoArguments, Prompt
from http_mcp.mcp_types.content import TextContent
from http_mcp.mcp_types.prompts import PromptMessage
def help_prompt_with_context(args: Arguments[NoArguments]) -> tuple[PromptMessage, ...]:
"""Use this prompt to get help with access to context."""
# You can still access request and state
context = args.get_state_key("context", Context)
return (
PromptMessage(
role="user",
content=TextContent(text="You are a helpful assistant."),
),
)
PROMPTS = (
Prompt(
func=help_prompt_with_context,
arguments_type=NoArguments,
),
)
Prompts with Lifespan State
from pydantic import BaseModel, Field
from http_mcp.mcp_types.content import TextContent
from http_mcp.mcp_types.prompts import PromptMessage
from http_mcp.types import Arguments, Prompt
from app.context import Context
class GetAdvice(BaseModel):
topic: str = Field(description="The topic to get advice on")
def get_advice_with_context(args: Arguments[GetAdvice]) -> tuple[PromptMessage, ...]:
"""Get advice on a topic with context awareness."""
# Access the context from lifespan state
context = args.get_state_key("context", Context)
called_tools = context.get_called_tools()
template = """
You are a helpful assistant that can give advice on {topic}.
Previously called tools: {tools}
"""
return (
PromptMessage(
role="user",
content=TextContent(
text=template.format(
topic=args.inputs.topic,
tools=", ".join(called_tools) if called_tools else "none"
)
)
),
)
PROMPTS_WITH_CONTEXT = (
Prompt(
func=get_advice_with_context,
arguments_type=GetAdvice,
),
)
Prompts with Authorization Scopes
You can restrict prompt access based on authentication scopes:
from http_mcp.types import Arguments, NoArguments, Prompt
from http_mcp.mcp_types.content import TextContent
from http_mcp.mcp_types.prompts import PromptMessage
def private_prompt(args: Arguments[NoArguments]) -> tuple[PromptMessage, ...]:
"""Private prompt that is only accessible to authenticated users."""
return (
PromptMessage(
role="user",
content=TextContent(text="This is a private prompt."),
),
)
def admin_prompt(args: Arguments[NoArguments]) -> tuple[PromptMessage, ...]:
"""Admin prompt accessible to users with admin or superuser scope."""
return (
PromptMessage(
role="user",
content=TextContent(text="This is an admin prompt."),
),
)
PROMPTS = (
Prompt(
func=private_prompt,
arguments_type=NoArguments,
scopes=("private",), # Only accessible with 'private' scope
),
Prompt(
func=admin_prompt,
arguments_type=NoArguments,
scopes=("admin", "superuser"), # Accessible with either scope
),
)
Note: You need to set up authentication middleware in your Starlette app for
scopes to work properly.
STDIO Transport
In addition to HTTP transport, the server supports STDIO transport for
communication. This is useful for command-line applications and integrations
that communicate through standard input/output.
Using STDIO Transport
import asyncio
from http_mcp.server import MCPServer
from app.tools import TOOLS
from app.prompts import PROMPTS
mcp_server = MCPServer(
tools=TOOLS,
prompts=PROMPTS,
name="test",
version="1.0.0"
)
# Run the server with STDIO transport
async def main() -> None:
request_headers = {
"Authorization": "Bearer your-token-here",
"X-Custom-Header": "value",
}
await mcp_server.serve_stdio(request_headers)
asyncio.run(main())
The request_headers parameter allows you to pass headers that will be included
in the request context, enabling authentication and other header-based features
even when using STDIO transport.
Authentication and Authorization
The library integrates with Starlette's authentication system to provide
scope-based authorization for tools and prompts.
Setting Up Authentication Middleware
import contextlib
from collections.abc import AsyncIterator
from typing import TypedDict
from starlette.applications import Starlette
from starlette.authentication import (
AuthCredentials,
AuthenticationBackend,
BaseUser,
SimpleUser,
)
from starlette.middleware import Middleware
from starlette.middleware.authentication import AuthenticationMiddleware
from starlette.requests import HTTPConnection
from http_mcp.server import MCPServer
from app.context import Context
from app.tools import TOOLS
from app.prompts import PROMPTS
class BasicAuthBackend(AuthenticationBackend):
def __init__(self, granted_scopes: tuple[str, ...] = ("authenticated",)) -> None:
self.granted_scopes = granted_scopes
super().__init__()
async def authenticate(
self, conn: HTTPConnection
) -> tuple[AuthCredentials, BaseUser] | None:
# Implement your authentication logic here
# For example, check Bearer token, API key, etc.
auth_header = conn.headers.get("Authorization")
if not auth_header:
return None
# Validate token and return credentials with scopes
return AuthCredentials(self.granted_scopes), SimpleUser("username")
class State(TypedDict):
context: Context
@contextlib.asynccontextmanager
async def lifespan(_app: Starlette) -> AsyncIterator[State]:
yield {"context": Context()}
mcp_server = MCPServer(
tools=TOOLS,
prompts=PROMPTS,
name="test",
version="1.0.0"
)
app = Starlette(
lifespan=lifespan,
middleware=[
Middleware(
AuthenticationMiddleware,
backend=BasicAuthBackend(granted_scopes=("private", "admin")),
),
],
)
app.mount("/mcp", mcp_server.app)
How Scopes Work
Authentication Middleware: The middleware authenticates each request and
assigns scopes to the user through AuthCredentials.
Tool/Prompt Scopes: When defining tools or prompts, you can specify
required scopes using the scopes parameter.
Access Control: The server automatically filters tools and prompts based
on the user's granted scopes. Tools and prompts without the required scopes
are not visible in listings and cannot be invoked.
Multiple Scopes: If you specify multiple scopes (e.g.,
scopes=("admin", "superuser")), the user needs at least one of those scopes
to access the tool or prompt.
API Reference
Tool Class
The Tool class is used to define tools that can be invoked by clients.
Parameters:
func: The function to be invoked. Can be sync or async. The function can
either:
inputs: The Pydantic model class for input validation. Use type(None) or
NoArguments for tools without inputs
output: The Pydantic model class for output validation
return_error_message (bool): If True, tool errors return ErrorMessage
instead of raising exceptions (default: False)
scopes (tuple[str, ...]): Required authentication scopes for accessing this
tool (default: empty tuple)
Properties:
name: The function name (derived from func.__name__)
title: A human-readable title (derived from the function name)
description: The function's docstring
input_schema: JSON schema for the input parameters
output_schema: JSON schema for the output
Prompt Class
The Prompt class is used to define prompts that can be invoked by clients.
Parameters:
func: The function to be invoked. Can be sync or async. The function can
either:
arguments_type: The Pydantic model class for argument validation. Use
type(None) or NoArguments for prompts without arguments
scopes (tuple[str, ...]): Required authentication scopes for accessing this
prompt (default: empty tuple)
Properties:
name: The function name (derived from func.__name__)
title: A human-readable title (derived from the function name)
description: The function's docstring
arguments: Tuple of PromptArgument objects defining the prompt's arguments
Arguments Class
The Arguments class is passed to tool and prompt functions to provide access
to inputs, request, and state.
Parameters:
Methods:
NoArguments Class
An empty Pydantic model that can be used as a clearer alternative to
type(None) when defining tools or prompts without arguments.
from http_mcp.types import NoArguments
# Use this instead of type(None)
Tool(func=my_func, inputs=NoArguments, output=MyOutput)
Installation
Install the package using pip or uv:
or
License
This project is licensed under the MIT License. See the LICENSE file for
details.
