Model Context Protocol (MCP)

What is the Model Context Protocol?

Model Context Protocol, or MCP, is an open and standardized protocol. You can use it to create structured and reliable communication between client software and language model (LLM) servers. Unlike regular APIs, MCP gives you a consistent way to exchange context, tools, and resources. This means you can connect AI systems from different companies without running into compatibility issues. MCP defines how to package and send not just prompts, but also extra information like metadata, tool descriptions, and resource links. This makes communication predictable and easy to expand when you need to add new features.

How MCP Connects to Python Server Development

When you use MCP in Python server projects, you avoid the confusion that comes from working with custom or unique interfaces. MCP-compliant servers know how to read and handle these structured requests. This approach helps you spend less time on integration and makes your code easier to maintain. You can quickly build AI applications, scale them up, and work with any client that supports MCP, no matter which AI system it uses. MCP’s clear way of defining context, resources, and tools means you write less translation code and build on a solid base of reusable components.

Core Principles and Architecture

MCP has several main parts:

• Servers: These are the systems that run MCP endpoints. They accept context details, tool requests, and resource calls from clients.
• Resources and Tools: These are modular features, like calculators or search engines, that the server makes available through MCP. Clients and LLMs can use them as needed.
• Context and Prompts: MCP lets you send detailed context, including user instructions, conversation history, and extra information. This supports more accurate and personalized model responses.
• Extensibility: You can expand MCP by adding your own tools, resources, or ways to structure context. This makes it easy to support new AI workflows as your needs change.

When you build with MCP’s schema-based approach, you set up your Python projects for future changes. MCP-compliant servers can work smoothly with new LLMs and AI tools as they come out. This protocol helps you focus on structure, adaptability, and making sure all your AI systems can work together.

Python Libraries for MCP Server Foundations

Building a Model Context Protocol (MCP) server in Python works best when you use specific libraries that support the protocol, handle requests smoothly, and scale well with more users or tasks.

MCP Python SDK

The MCP Python SDK is the main tool you need to create servers that follow MCP rules. This toolkit gives you features for checking data formats (schema validation), managing context, and handling how the protocol works. With it, you can define resources and set up tools quickly. The SDK helps make sure your server matches the latest MCP standards. Because it handles most of the protocol logic for you, you spend less time writing custom code and updating your server as the protocol changes.

Core Web Frameworks: FastAPI and Flask

FastAPI

FastAPI is a current and fast web framework that works well for MCP servers. It supports asynchronous programming, which means your server can work on many requests at the same time without getting stuck. FastAPI creates OpenAPI documentation automatically and checks your data using pydantic. Because it uses an ASGI (Asynchronous Server Gateway Interface) setup and avoids blocking tasks, FastAPI can handle a lot of simultaneous requests. This makes it a strong choice for applications that use artificial intelligence or need to manage a lot of context at once.

Flask

Flask is another popular web framework. Many people choose it because it is simple and easy to use. By default, Flask handles one request at a time, which works well for simple applications or when tasks do not need to run at the same time. If you want Flask to handle more than one task at once, you can add extra libraries. Flask is a good option when you want to build a quick prototype or your server does not need to handle many users at the same time.

Asynchronous Concurrency: asyncio and trio

asyncio

asyncio comes with Python and lets you write asynchronous code. It allows you to use async and await commands, helping your server respond to many requests and do background work without waiting for one task to finish before starting another. If you use FastAPI or build your own ASGI application, asyncio helps you manage several jobs at once, like running tasks in the background or making calls to other systems, without needing extra threads.

trio

trio is another library for asynchronous programming but with some extra features. It uses structured concurrency, which makes it easier to organize and cancel groups of tasks safely. trio improves how errors are handled and makes complicated async work simpler to manage. Developers choose trio for MCP servers that need careful control over many tasks running together.

Foundation for Scalable MCP Servers

When you put together the MCP Python SDK with FastAPI (or Flask) and add either asyncio or trio, you get a solid setup for your MCP server. This combination supports organized communication and prepares your server for more advanced features, new connections, and running at a larger scale.

Advanced Libraries & Tools for Enhanced Server Functionality

Data Serialization and Validation

To keep your protocol-based server reliable, you need precise data validation. You can use pydantic, which reads Python type annotations to check and parse data during runtime. This tool requires little extra processing power and works well for creating strict message formats for MCP messages and tool inputs. Pydantic uses up-to-date parsing methods, and benchmarks show it can validate typical data models in less than a millisecond. This helps you catch wrong data types and block requests that do not follow your rules.
marshmallow is another tool for handling how data moves in and out of your system. It supports custom data fields, organizes complex data into nested structures, and lets you run extra steps before or after processing. This is useful when you need to transform or clean up data as it comes into your MCP server.

Real-Time Communication: Websockets and SSE

Many interactive AI systems need real-time updates. With websockets, your server and clients can send messages back and forth at any time using one TCP connection. This setup lets you stream responses, send live updates from tools, or work on shared model tasks with others. Tests and studies show that websocket connections usually keep delays under 50 milliseconds, which is much faster than long-polling or regular HTTP requests for ongoing communication.

If you only need to send updates from the server to the client, Server-Sent Events (SSE) can help. SSE uses simple HTTP connections, which most browsers support. It works well for sending one-way messages, like notifications or updates, and keeps server resources low when you do not need two-way communication.

Security and Authentication

To keep model contexts and user data safe, you need strong authentication. Authlib helps you set up OAuth2 and OpenID Connect. These are common methods for letting users log in securely and for managing tokens that control access. Authlib follows accepted standards and makes it easier to connect with different identity providers, while reducing weak spots in your security.

For session management, PyJWT lets you use JSON Web Tokens. These tokens are cryptographically signed, so you can quickly check who a user is and what they can do, without looking things up in a database each time. PyJWT supports advanced signing methods like RS256 and HS512, meeting strict security needs shown in industry research and guidelines.

When you use pydantic, marshmallow, websockets, SSE, Authlib, and PyJWT in your MCP server, you set up strong data checking, fast real-time communication, and secure authentication. Each library handles a specific job, which helps you keep your server modular, easy to maintain, and ready for production.

Integration Strategies for MCP Servers

Efficient integration helps MCP servers interact with outside services, manage data, and deploy reliably. Here, you will find specific strategies, clear explanations, and practical examples for each key library used in modern Python-based MCP server development.

Connecting to External APIs

MCP servers often need data from third-party sources to improve model context. You can use the requests library for synchronous HTTP calls when blocking operations do not cause issues, such as during server startup or low-traffic periods. If your server needs to handle many requests at once or avoid blocking, the httpx library offers asynchronous HTTP features. HTTPX supports connection pooling and HTTP/2, which improves speed and data handling for busy servers (see HTTPX benchmarks for performance details).

Example:

• Call requests.get() to fetch resources in scripts or tools that run synchronously.
• Use await httpx.AsyncClient().get() inside asynchronous FastAPI endpoints to fetch data in parallel.

Database Integration

MCP servers often need to store and manage data over time. For relational databases, SQLAlchemy provides an Object Relational Mapper (ORM). This tool lets you write Python code to create, read, update, and delete database records, and it handles complex queries and database changes. SQLAlchemy’s ORM shields you from writing raw SQL, which helps prevent coding mistakes and makes maintenance easier (see SQLAlchemy documentation and studies on ORM benefits).

For applications that use asynchronous programming, asyncpg offers direct access to PostgreSQL with full async support. This library works well in situations where you need to handle many database connections at the same time, like in FastAPI-powered MCP servers. Benchmarks show asyncpg can reduce delays and handle more requests per second compared to synchronous database drivers.

Example:

• Use SQLAlchemy’s ORM to record user actions or tool usage in a database.
• Use asyncpg for event-driven tasks that require non-blocking database operations.

Production Deployment

To run MCP APIs for many users, uvicorn works well as an ASGI server for FastAPI apps. Uvicorn uses asyncio to handle many requests at once. For servers built with WSGI frameworks like Flask, gunicorn manages several worker processes, which helps your application stay reliable under heavy use. Scientific tests show uvicorn’s event loop works efficiently for input/output-heavy, asynchronous workloads. Gunicorn fits traditional, synchronous applications well.

You can use Docker to package your server and its dependencies into a single, reproducible image. Docker makes the server easy to move, helps with orchestration tools such as Kubernetes, and supports reliable continuous integration and delivery (CI/CD) processes. Research shows Docker reduces setup errors and supports easy scaling across multiple machines.

Example:

• Start a FastAPI MCP server with uvicorn main:app --host 0.0.0.0 --port 80.
• Place your server code in a Dockerfile to build consistent images for any environment.

You can combine requests or httpx for API calls, SQLAlchemy or asyncpg for data storage, uvicorn or gunicorn for serving, and Docker for deployment. These strategies help MCP servers connect with outside systems, store data efficiently, and run reliably in real-world production environments.

Practical Example – Building a Simple MCP Server

Step 1: Install Required Packages

First, use pip to add all the libraries you need for the MCP server:

pip install fastapi uvicorn pydantic mcp-sdk

Step 2: Define a Calculator Tool and Create the MCP Server

You will use FastAPI to handle HTTP requests, pydantic to check and structure input data, and the MCP Python SDK to follow the MCP protocol.

from fastapi import FastAPI  
from pydantic import BaseModel  
from mcp_sdk import MCPServer, Tool

app = FastAPI()  
mcp_server = MCPServer(app)

class AddInput(BaseModel):  
    a: float  
    b: float

@Tool(name="add", input_model=AddInput)  
def add(inputs: AddInput):  
    return {"result": inputs.a + inputs.b}

mcp_server.register_tool(add)

Explanation:

• FastAPI sets up the web app and the HTTP routes you need.
• pydantic uses the AddInput class to make sure the inputs for the tool have the right types and structure.
• The Tool decorator from the MCP SDK publishes the add function as an MCP resource that fits the protocol.
• MCPServer connects your tool to FastAPI and creates MCP-compatible endpoints automatically.

Step 3: Run the Server

Start the ASGI server with uvicorn to make the MCP endpoints available:

uvicorn main:app --reload

How It Works

When the server gets a properly formatted MCP request for the add tool, FastAPI routes it to the right function. pydantic checks the data to make sure it is correct. The MCP SDK handles all protocol rules. The add tool then calculates the sum and sends back a JSON object with the result. You can add more tools by creating new input models and functions, then registering them with the MCP server.

This example gives you a full setup for a simple, standards-following MCP server. You use FastAPI, pydantic, the MCP Python SDK, and uvicorn. You can use this pattern to build larger MCP servers with more tools and features.