Build Your First MCP Server in Python (Stateless Spec Edition)

來源原標題

Build Your First MCP Server in Python (Stateless Spec Edition)

作者

Kanwal Mehreen

原始連結

https://www.kdnuggets.com/build-your-first-mcp-server-in-python-stateless-spec-edition

摘要原文

Build Your First MCP Server in Python (Stateless Spec Edition) MCP just got simpler. Learn how to build a stateless MCP server in Python and expose tools, resources, and prompts over HTTP. By Kanwal Mehreen , KDnuggets Technical Editor & Content Specialist on October 9, 2026 in Python The Model Context Protocol changed in an important way this summer. With the 2026-07-28 MCP specification , the protocol core is now stateless : modern clients no longer need to establish a protocol session before making requests, and servers no longer rely on Mcp-Session-Id for ordinary requests. That makes MCP servers much easier to scale behind normal HTTP infrastructure. At the same time, the official Python SDK has moved to v2 as its current stable line and provides a higher-level MCPServer API for defining tools, resources, and prompts with regular Python functions. In this tutorial, we will build a small but complete MCP server in Python, run it over Streamable HTTP, inspect it locally, and connect to it with a Python MCP client. Let's get started. What Are We Building? We will create a small developer knowledge-base server . It will expose three MCP primitives: Tool: search_kb(query, limit) Resource: kb://articles Prompt: draft_support_reply(customer_message) The server will contain no user session state. Every request will contain everything required to process it, which makes it a good example of the new stateless MCP model. Conceptually: LLM Host | | MCP request v +-----------------------+ | Python MCP Server | | | | search_kb() | | kb://articles | | draft_support_reply() | +-----------------------+ Step 1: Creating the Project The current Python SDK requires Python 3.10 or newer. The official documentation recommends installing the CLI extra because it gives us the development command and MCP Inspector workflow. Using uv : mkdir first-mcp-server cd first-mcp-server uv init uv add "mcp[cli]" Or with pip : pip install "mcp[cli]" Your project can be as small as: first-mcp-server/ ├── server.py └── client.py No framework boilerplate is required. Step 2: Creating Your First MCP Server Create server.py : from mcp.server import MCPServer mcp = MCPServer( "Developer Support KB", instructions=( "Use the knowledge-base tools to answer support questions. " "Prefer retrieved KB information over guessing." ), ) MCPServer is the high-level server API in the current Python SDK. For most servers, this is the API you want. The SDK also exposes a lower-level Server class, but that is intended for cases where you need exact control over schemas, protocol metadata, or custom methods. Now let's give our server some data. ARTICLES = [ { "id": "python-env", "title": "Creating a Python virtual environment", "body": ( "Create a virtual environment with `python -m venv .venv`, " "then activate it before installing dependencies." ), }, { "id": "reset-password", "title": "Resetting your password", "body": ( "Open Account Settings, choose Security, and select " "Reset Password. A verification email will be sent." ), }, { "id": "api-rate-limit", "title": "Understanding API rate limits", "body": ( "API rate limits restrict the number of requests allowed " "within a time window. Clients should retry using " "exponential backoff after receiving a rate-limit response." ), }, ] So far this is just Python. The interesting part begins when we expose functions through MCP. Step 3: Adding an MCP Tool An MCP tool is a function the model can decide to call. Add this to server.py : @mcp.tool() def search_kb(query: str, limit: int = 3) -> list[dict[str, str]]: """Search the support knowledge base. Args: query: Words or phrases to search for. limit: Maximum number of articles to return. """ query = query.lower() matches = [] for article in ARTICLES: searchable_text = ( article["title"] + " " + article["body"] ).lower() if query in searchable_text: matches.append(article) return matches[:limit] Notice what we did not write. There is no JSON Schema. There is no manually written tool manifest. There is no argument parser. The SDK derives the tool definition from the Python function itself. Its type hints become the MCP input schema, and defaults such as: limit: int = 3 make parameters optional in the generated schema. The official SDK documentation uses exactly this pattern. Conceptually, your function: def search_kb( query: str, limit: int = 3 ) becomes something similar to: { "name": "search_kb", "inputSchema": { "type": "object", "properties": { "query": { "type": "string" }, "limit": { "type": "integer", "default": 3 } }, "required": ["query"] } } This is one of the reasons MCP development in Python feels pleasantly ordinary: your function signature is effectively your interface definition. Step 4: Adding a Resource Tools are actions the model can call. Resources are different. They expose information that the host application can load into context. Add: @mcp.resource("kb://articles") def list_articles() -> str: """Return the available knowledge-base articles.""" lines = [] for article in ARTICLES: lines.append( f"{article['id']}: {article['title']}" ) return "\n".join(lines) The resource has the URI: kb://articles A client can read it without invoking a tool. Resource ≈ data that can be read Tool ≈ function that can perform work The SDK documentation roughly compares resources with GET -like behavior and tools with action-oriented POST -like behavior. Step 5: Adding an MCP Prompt We can also expose a reusable prompt template. @mcp.prompt() def draft_support_reply(customer_message: str) -> str: """Create a prompt for drafting a concise support response.""" return f""" You are a technical support assistant. Write a concise and helpful response to this customer message: {customer_message} Use the support knowledge base when relevant. Do not invent product policies. """.strip() Again, this is just a Python function plus a decorator. Prompts are generally initiated by the user or host rather than autonomously invoked by the model. The current SDK supports tools, resources, and prompts through the same decorator-oriented server interface. At this point, server.py looks like this: from mcp.server import MCPServer mcp = MCPServer( "Developer Support KB", instructions=( "Use the knowledge-base tools to answer support questions. " "Prefer retrieved KB information over guessing." ), ) ARTICLES = [ { "id": "python-env", "title": "Creating a Python virtual environment", "body": ( "Create a virtual environment with `python -m venv .venv`, " "then activate it before installing dependencies." ), }, { "id": "reset-password", "title": "Resetting your password", "body": ( "Open Account Settings, choose Security, and select " "Reset Password. A verification email will be sent." ), }, { "id": "api-rate-limit", "title": "Understanding API rate limits", "body": ( "API rate limits restrict the number of requests allowed " "within a time window. Clients should retry using " "exponential backoff after receiving a rate-limit response." ), }, ] @mcp.tool() def search_kb( query: str, limit: int = 3, ) -> list[dict[str, str]]: """Search the support knowledge base.""" query = query.lower() matches = [] for article in ARTICLES: searchable_text = ( article["title"] + " " + article["body"] ).lower() if query in searchable_text: matches.append(article) return matches[:limit] @mcp.resource("kb://articles") def list_articles() -> str: """Return the available knowledge-base articles.""" return "\n".join( f"{article['id']}: {article['title']}" for article in ARTICLES ) @mcp.prompt() def draft_support_reply( customer_message: str, ) -> str: """Create a support-response prompt.""" return f""" You are a technical support assistant. Write a concise and helpful response to this customer message: {customer_message} Use the support knowledge base when relevant. Do not invent product policies. """.strip() if __name__ == "__main__": mcp.run("streamable-http") That is a complete network-accessible MCP application.