Gotas Commerce MCP Server

https://github.com/modelcontextprotocol/python-sdk MCP Python SDK Python implementation of the Model Context Protocol (MCP) PyPI MIT licensed Python Version Documentation Specification GitHub Discussions Table of Contents MCP Python SDK Overview Installation Adding MCP to your python project Running the standalone MCP development tools Quickstart What is MCP? Core Concepts Server Resources Tools Prompts Images Context Running Your Server Development Mode Claude Desktop Integration Direct Execution Mounting to an Existing ASGI Server Examples Echo Server SQLite Explorer Advanced Usage Low-Level Server Writing MCP Clients MCP Primitives Server Capabilities Documentation Contributing License Overview The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to: Build MCP clients that can connect to any MCP server Create MCP servers that expose resources, prompts and tools Use standard transports like stdio, SSE, and Streamable HTTP Handle all MCP protocol messages and lifecycle events Installation Adding MCP to your python project We recommend using uv to manage your Python projects. If you haven't created a uv-managed project yet, create one: uv init mcp-server-demo cd mcp-server-demo Then add MCP to your project dependencies: uv add "mcp[cli]" Alternatively, for projects using pip for dependencies: pip install "mcp[cli]" Running the standalone MCP development tools To run the mcp command with uv: uv run mcp Quickstart Let's create a simple MCP server that exposes a calculator tool and some data: # server.py from mcp.server.fastmcp import FastMCP # Create an MCP server mcp = FastMCP("Demo") # Add an addition tool @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers""" return a + b # Add a dynamic greeting resource @mcp.resource("greeting://{name}") def get_greeting(name: str) -> str: """Get a personalized greeting""" return f"Hello, {name}!" You can install this server in Claude Desktop and interact with it right away by running: mcp install server.py Alternatively, you can test it with the MCP Inspector: mcp dev server.py What is MCP? The Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions. MCP servers can: Expose data through Resources (think of these sort of like GET endpoints; they are used to load information into the LLM's context) Provide functionality through Tools (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect) Define interaction patterns through Prompts (reusable templates for LLM interactions) And more! Core Concepts Server The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing: # Add lifespan support for startup/shutdown with strong typing from contextlib import asynccontextmanager from collections.abc import AsyncIterator from dataclasses import dataclass from fake_database import Database # Replace with your actual DB type from mcp.server.fastmcp import Context, FastMCP # Create a named server mcp = FastMCP("My App") # Specify dependencies for deployment and development mcp = FastMCP("My App", dependencies=["pandas", "numpy"]) @dataclass class AppContext: db: Database @asynccontextmanager async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]: """Manage application lifecycle with type-safe context""" # Initialize on startup db = await Database.connect() try: yield AppContext(db=db) finally: # Cleanup on shutdown await db.disconnect() # Pass lifespan to server mcp = FastMCP("My App", lifespan=app_lifespan) # Access type-safe lifespan context in tools @mcp.tool() def query_db(ctx: Context) -> str: """Tool that uses initialized resources""" db = ctx.request_context.lifespan_context.db return db.query() Resources Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects: from mcp.server.fastmcp import FastMCP mcp = FastMCP("My App") @mcp.resource("config://app") def get_config() -> str: """Static configuration data""" return "App configuration here" @mcp.resource("users://{user_id}/profile") def get_user_profile(user_id: str) -> str: """Dynamic user data""" return f"Profile data for user {user_id}" Tools Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects: import httpx from mcp.server.fastmcp import FastMCP mcp = FastMCP("My App") @mcp.tool() def calculate_bmi(weight_kg: float, height_m: float) -> float: """Calculate BMI given weight in kg and height in meters""" return weight_kg / (height_m**2) @mcp.tool() async def fetch_weather(city: str) -> str: """Fetch current weather for a city""" async with httpx.AsyncClient() as client: response = await client.get(f"https://api.weather.com/{city}") return response.text Prompts Prompts are reusable templates that help LLMs interact with your server effectively: from mcp.server.fastmcp import FastMCP from mcp.server.fastmcp.prompts import base mcp = FastMCP("My App") @mcp.prompt() def review_code(code: str) -> str: return f"Please review this code:\n\n{code}" @mcp.prompt() def debug_error(error: str) -> list[base.Message]: return [ base.UserMessage("I'm seeing this error:"), base.UserMessage(error), base.AssistantMessage("I'll help debug that. What have you tried so far?"), ] Images FastMCP provides an Image class that automatically handles image data: from mcp.server.fastmcp import FastMCP, Image from PIL import Image as PILImage mcp = FastMCP("My App") @mcp.tool() def create_thumbnail(image_path: str) -> Image: """Create a thumbnail from an image""" img = PILImage.open(image_path) img.thumbnail((100, 100)) return Image(data=img.tobytes(), format="png") Context The Context object gives your tools and resources access to MCP capabilities: from mcp.server.fastmcp import FastMCP, Context mcp = FastMCP("My App") @mcp.tool() async def long_task(files: list[str], ctx: Context) -> str: """Process multiple files with progress tracking""" for i, file in enumerate(files): ctx.info(f"Processing {file}") await ctx.report_progress(i, len(files)) data, mime_type = await ctx.read_resource(f"file://{file}") return "Processing complete" Authentication Authentication can be used by servers that want to expose tools accessing protected resources. mcp.server.auth implements an OAuth 2.0 server interface, which servers can use by providing an implementation of the OAuthServerProvider protocol. mcp = FastMCP("My App", auth_provider=MyOAuthServerProvider(), auth=AuthSettings( issuer_url="https://myapp.com", revocation_options=RevocationOptions( enabled=True, ), client_registration_options=ClientRegistrationOptions( enabled=True, valid_scopes=["myscope", "myotherscope"], default_scopes=["myscope"], ), required_scopes=["myscope"], ), ) See OAuthServerProvider for more details. Running Your Server Development Mode The fastest way to test and debug your server is with the MCP Inspector: mcp dev server.py # Add dependencies mcp dev server.py --with pandas --with numpy # Mount local code mcp dev server.py --with-editable . Claude Desktop Integration Once your server is ready, install it in Claude Desktop: mcp install server.py # Custom name mcp install server.py --name "My Analytics Server" # Environment variables mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://... mcp install server.py -f .env Direct Execution For advanced scenarios like custom deployments: from mcp.server.fastmcp import FastMCP mcp = FastMCP("My App") if __name__ == "__main__": mcp.run() Run it with: python server.py # or mcp run server.py Streamable HTTP Transport Note: Streamable HTTP transport is superseding SSE transport for production deployments. from mcp.server.fastmcp import FastMCP # Stateful server (maintains session state) mcp = FastMCP("StatefulServer") # Stateless server (no session persistence) mcp = FastMCP("StatelessServer", stateless_http=True) # Run server with streamable_http transport mcp.run(transport="streamable-http") You can mount multiple FastMCP servers in a FastAPI application: # echo.py from mcp.server.fastmcp import FastMCP mcp = FastMCP(name="EchoServer", stateless_http=True) @mcp.tool(description="A simple echo tool") def echo(message: str) -> str: return f"Echo: {message}" # math.py from mcp.server.fastmcp import FastMCP mcp = FastMCP(name="MathServer", stateless_http=True) @mcp.tool(description="A simple add tool") def add_two(n: int) -> str: return n + 2 # main.py from fastapi import FastAPI from mcp.echo import echo from mcp.math import math app = FastAPI() # Use the session manager's lifespan app = FastAPI(lifespan=lambda app: echo.mcp.session_manager.run()) app.mount("/echo", echo.mcp.streamable_http_app()) app.mount("/math", math.mcp.streamable_http_app()) For low level server with Streamable HTTP implementations, see: Stateful server: examples/servers/simple-streamablehttp/ Stateless server: examples/servers/simple-streamablehttp-stateless/ The streamable HTTP transport supports: Stateful and stateless operation modes Resumability with event stores JSON or SSE response formats Better scalability for multi-node deployments Mounting to an Existing ASGI Server Note: SSE transport is being superseded by Streamable HTTP transport. You can mount the SSE server to an existing ASGI server using the sse_app method. This allows you to integrate the SSE server with other ASGI applications. from starlette.applications import Starlette from starlette.routing import Mount, Host from mcp.server.fastmcp import FastMCP mcp = FastMCP("My App") # Mount the SSE server to the existing ASGI server app = Starlette( routes=[ Mount('/', app=mcp.sse_app()), ] ) # or dynamically mount as host app.router.routes.append(Host('mcp.acme.corp', app=mcp.sse_app())) When mounting multiple MCP servers under different paths, you can configure the mount path in several ways: from starlette.applications import Starlette from starlette.routing import Mount from mcp.server.fastmcp import FastMCP # Create multiple MCP servers github_mcp = FastMCP("GitHub API") browser_mcp = FastMCP("Browser") curl_mcp = FastMCP("Curl") search_mcp = FastMCP("Search") # Method 1: Configure mount paths via settings (recommended for persistent configuration) github_mcp.settings.mount_path = "/github" browser_mcp.settings.mount_path = "/browser" # Method 2: Pass mount path directly to sse_app (preferred for ad-hoc mounting) # This approach doesn't modify the server's settings permanently # Create Starlette app with multiple mounted servers app = Starlette( routes=[ # Using settings-based configuration Mount("/github", app=github_mcp.sse_app()), Mount("/browser", app=browser_mcp.sse_app()), # Using direct mount path parameter Mount("/curl", app=curl_mcp.sse_app("/curl")), Mount("/search", app=search_mcp.sse_app("/search")), ] ) # Method 3: For direct execution, you can also pass the mount path to run() if __name__ == "__main__": search_mcp.run(transport="sse", mount_path="/search") For more information on mounting applications in Starlette, see the Starlette documentation. Examples Echo Server A simple server demonstrating resources, tools, and prompts: from mcp.server.fastmcp import FastMCP mcp = FastMCP("Echo") @mcp.resource("echo://{message}") def echo_resource(message: str) -> str: """Echo a message as a resource""" return f"Resource echo: {message}" @mcp.tool() def echo_tool(message: str) -> str: """Echo a message as a tool""" return f"Tool echo: {message}" @mcp.prompt() def echo_prompt(message: str) -> str: """Create an echo prompt""" return f"Please process this message: {message}" SQLite Explorer A more complex example showing database integration: import sqlite3 from mcp.server.fastmcp import FastMCP mcp = FastMCP("SQLite Explorer") @mcp.resource("schema://main") def get_schema() -> str: """Provide the database schema as a resource""" conn = sqlite3.connect("database.db") schema = conn.execute("SELECT sql FROM sqlite_master WHERE type='table'").fetchall() return "\n".join(sql[0] for sql in schema if sql[0]) @mcp.tool() def query_data(sql: str) -> str: """Execute SQL queries safely""" conn = sqlite3.connect("database.db") try: result = conn.execute(sql).fetchall() return "\n".join(str(row) for row in result) except Exception as e: return f"Error: {str(e)}" Advanced Usage Low-Level Server For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API: from contextlib import asynccontextmanager from collections.abc import AsyncIterator from fake_database import Database # Replace with your actual DB type from mcp.server import Server @asynccontextmanager async def server_lifespan(server: Server) -> AsyncIterator[dict]: """Manage server startup and shutdown lifecycle.""" # Initialize resources on startup db = await Database.connect() try: yield {"db": db} finally: # Clean up on shutdown await db.disconnect() # Pass lifespan to server server = Server("example-server", lifespan=server_lifespan) # Access lifespan context in handlers @server.call_tool() async def query_db(name: str, arguments: dict) -> list: ctx = server.request_context db = ctx.lifespan_context["db"] return await db.query(arguments["query"]) The lifespan API provides: A way to initialize resources when the server starts and clean them up when it stops Access to initialized resources through the request context in handlers Type-safe context passing between lifespan and request handlers import mcp.server.stdio import mcp.types as types from mcp.server.lowlevel import NotificationOptions, Server from mcp.server.models import InitializationOptions # Create a server instance server = Server("example-server") @server.list_prompts() async def handle_list_prompts() -> list[types.Prompt]: return [ types.Prompt( name="example-prompt", description="An example prompt template", arguments=[ types.PromptArgument( name="arg1", description="Example argument", required=True ) ], ) ] @server.get_prompt() async def handle_get_prompt( name: str, arguments: dict[str, str] | None ) -> types.GetPromptResult: if name != "example-prompt": raise ValueError(f"Unknown prompt: {name}") return types.GetPromptResult( description="Example prompt", messages=[ types.PromptMessage( role="user", content=types.TextContent(type="text", text="Example prompt text"), ) ], ) async def run(): async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): await server.run( read_stream, write_stream, InitializationOptions( server_name="example", server_version="0.1.0", capabilities=server.get_capabilities( notification_options=NotificationOptions(), experimental_capabilities={}, ), ), ) if __name__ == "__main__": import asyncio asyncio.run(run()) Writing MCP Clients The SDK provides a high-level client interface for connecting to MCP servers using various transports: from mcp import ClientSession, StdioServerParameters, types from mcp.client.stdio import stdio_client # Create server parameters for stdio connection server_params = StdioServerParameters( command="python", # Executable args=["example_server.py"], # Optional command line arguments env=None, # Optional environment variables ) # Optional: create a sampling callback async def handle_sampling_message( message: types.CreateMessageRequestParams, ) -> types.CreateMessageResult: return types.CreateMessageResult( role="assistant", content=types.TextContent( type="text", text="Hello, world! from model", ), model="gpt-3.5-turbo", stopReason="endTurn", ) async def run(): async with stdio_client(server_params) as (read, write): async with ClientSession( read, write, sampling_callback=handle_sampling_message ) as session: # Initialize the connection await session.initialize() # List available prompts prompts = await session.list_prompts() # Get a prompt prompt = await session.get_prompt( "example-prompt", arguments={"arg1": "value"} ) # List available resources resources = await session.list_resources() # List available tools tools = await session.list_tools() # Read a resource content, mime_type = await session.read_resource("file://some/path") # Call a tool result = await session.call_tool("tool-name", arguments={"arg1": "value"}) if __name__ == "__main__": import asyncio asyncio.run(run()) Clients can also connect using Streamable HTTP transport: from mcp.client.streamable_http import streamablehttp_client from mcp import ClientSession async def main(): # Connect to a streamable HTTP server async with streamablehttp_client("example/mcp") as ( read_stream, write_stream, _, ): # Create a session using the client streams async with ClientSession(read_stream, write_stream) as session: # Initialize the connection await session.initialize() # Call a tool tool_result = await session.call_tool("echo", {"message": "hello"}) MCP Primitives The MCP protocol defines three core primitives that servers can implement: Primitive Control Description Example Use Prompts User-controlled Interactive templates invoked by user choice Slash commands, menu options Resources Application-controlled Contextual data managed by the client application File contents, API responses Tools Model-controlled Functions exposed to the LLM to take actions API calls, data updates Server Capabilities MCP servers declare capabilities during initialization: Capability Feature Flag Description prompts listChanged Prompt template management resources subscribe listChanged Resource exposure and updates tools listChanged Tool discovery and execution logging - Server logging configuration completion - Argument completion suggestions Documentation Model Context Protocol documentation Model Context Protocol specification Officially supported servers