In my previous blog, I discussed the challenges of dealing with noisy data. Now, it's time to get our hands dirty and start coding. The MCP, or Model Connection Protocol, is essentially the wiring that enables a language model to interact with external software without having to cram every command into the prompt. While there have been many discussions about MCP servers, they are, in simplest terms, small web services that inform the model about available tools and execute these tools when requested. In this first build log, we'll take a broader view of the MCP architecture, identify its key components, and then dive into creating the most basic MCP server using Python and FastAPI. We'll also compare this setup with the traditional Function Calling flow to understand their similarities and differences. So, why go through the trouble of building an MCP stack? Many of us have experimented with adding tools to models like GPT or Claude using the "old" method. This process typically involves several steps: Adding function schemas to the tools parameter. Since language models don’t natively support tools, you need to write a tool identification and argument wrapper on top of the model's completion call. Hoping the model or wrapper returns valid JSON. Verifying the JSON and, if it's incorrect, retrying the process or applying a regex fix. These steps can be cumbersome and error-prone. By building an MCP stack, we streamline the integration process, making it more efficient and reliable. Let's break down the MCP architecture and then work on a simple implementation. Key Components of MCP Architecture Model: The language model, such as GPT or Claude, which generates responses and interacts with the user. Tool Registry: A service or API that lists the available tools and their functionalities. Tool Executor: The component that runs the tools based on the model's requests. Communication Layer: The interface through which the model communicates with the Tool Registry and Tool Executor. Validation Layer: Ensures that the commands and data exchanged are valid and correct. Building a Basic MCP Server with Python and FastAPI To create a minimalistic MCP server, we'll use Python and FastAPI. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. Here’s a step-by-step guide to setting up a simple MCP server: Step 1: Install FastAPI and Uvicorn First, you need to install FastAPI and Uvicorn, the ASGI server that will run your FastAPI application. sh pip install fastapi uvicorn Step 2: Define the Tool Registry Next, define a list of available tools and their schemas. Each tool should have a unique identifier, a description, and the required parameters. ```python from typing import List, Dict, Any tools: List[Dict[str, Any]] = [ { "id": "search_web", "description": "Search the web for information.", "parameters": ["query"] }, { "id": "send_email", "description": "Send an email to a specified recipient.", "parameters": ["to", "subject", "body"] } ] ``` Step 3: Create the FastAPI Application Now, set up your FastAPI application to serve the tool registry and handle tool execution requests. ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel app = FastAPI() class ToolRequest(BaseModel): tool_id: str arguments: Dict[str, Any] @app.get("/tools") async def get_tools(): return tools @app.post("/execute_tool") async def execute_tool(request: ToolRequest): tool_id = request.tool_id arguments = request.arguments # Find the tool in the registry tool = next((t for t in tools if t["id"] == tool_id), None) if tool is None: raise HTTPException(status_code=404, detail="Tool not found") # Call the appropriate tool function if tool_id == "search_web": # Implement the search_web function return {"result": f"Web search results for {arguments['query']}"} elif tool_id == "send_email": # Implement the send_email function return {"result": f"Email sent to {arguments['to']} with subject {arguments['subject']}"} else: raise HTTPException(status_code=501, detail="Tool not implemented") ``` Step 4: Run the FastAPI Application Finally, run your FastAPI application using Uvicorn. This will start the server on http://127.0.0.1:8000. sh uvicorn your_app_name:app --reload Comparing MCP and Traditional Function Calling Both MCP and the traditional Function Calling approach aim to extend the capabilities of language models by integrating external tools. However, they differ in several key aspects: Simplicity: MCP provides a standardized and modular approach, making it easier to manage multiple tools and reduce code complexity. Reliability: The validation layer in MCP ensures that the commands and data are correct, reducing the likelihood of errors. Flexibility: MCP allows for dynamic tool management, enabling new tools to be added or removed without modifying the core model code. By adopting the MCP architecture, developers can focus more on enhancing functionality and less on handling low-level integration issues. This streamlined process leads to more robust and scalable systems, ultimately improving the user experience with language models. Conclusion Building an MCP stack is a valuable step for anyone looking to integrate external tools with language models efficiently and reliably. The architecture provides a clear separation of responsibilities, making it easier to manage and scale the integration process. With Python and FastAPI, creating even the simplest MCP server is straightforward and can serve as a foundation for more complex implementations. So, let's start coding and enhance the capabilities of our language models!