Model Communication Protocol (MCP): A Unified Approach to Interacting with LLMs

With the explosion of Large Language Models (LLMs) like OpenAI’s GPT, Anthropic’s Claude, Meta’s LLaMA, and Google’s Gemini, developers are facing the challenge of working with various APIs, data formats, and SDKs. Model Communication Protocol (MCP) provides a standardized layer to communicate with different LLMs through a single, unified interface.

In this article, we’ll explore:

·        What is MCP?

·        How to create an MCP server

·        How to integrate various LLMs (OpenAI, Claude, Gemini, etc.)

·        Real-life applications of MCP

·        Complete working code using FastAPI

 

What is MCP?

MCP (Model Communication Protocol) is a lightweight protocol that standardizes how we send prompts and receive responses from LLMs. Think of it like a universal translator or an API gateway for AI models.

MCP typically supports:

• Standardized request/response format
• Routing to the appropriate LLM backend
• Middleware for logging, caching, or pre-processing
• Model abstraction layer

 

How to Create an MCP Server

We'll use FastAPI to build the MCP server.

Directory Structure

mcp-server/

├── main.py

├── llms/

│   ├── openai_llm.py

│   ├── claude_llm.py

│   ├── gemini_llm.py

│   └── llama_llm.py

└── models/

    └── schema.py

Step 1: Install Dependencies

pip install fastapi uvicorn openai anthropic google-generativeai

 

Step 2: Define Schema

models/schema.py

from pydantic import BaseModel

from typing import Optional

 

class MCPRequest(BaseModel):

    model_provider: str  # openai, claude, gemini, llama

    prompt: str

    temperature: Optional[float] = 0.7

    max_tokens: Optional[int] = 300

 

class MCPResponse(BaseModel):

    response: str

Step 3: LLM Provider Wrappers

Each LLM has a unique SDK and call format. We wrap them.

llms/openai_llm.py

import openai

 

openai.api_key = "your-openai-key"

 

def chat(prompt, temperature=0.7, max_tokens=300):

    completion = openai.ChatCompletion.create(

        model="gpt-3.5-turbo",

        messages=[{"role": "user", "content": prompt}],

        temperature=temperature,

        max_tokens=max_tokens

    )

    return completion.choices[0].message["content"]

llms/claude_llm.py

import anthropic

 

client = anthropic.Anthropic(api_key="your-claude-key")

 

def chat(prompt, temperature=0.7, max_tokens=300):

    response = client.messages.create(

        model="claude-3-opus-20240229",

        messages=[{"role": "user", "content": prompt}],

        max_tokens=max_tokens,

        temperature=temperature

    )

    return response.content[0].text

llms/gemini_llm.py

import google.generativeai as genai

 

genai.configure(api_key="your-gemini-key")

 

def chat(prompt, temperature=0.7, max_tokens=300):

    model = genai.GenerativeModel("gemini-pro")

    response = model.generate_content(prompt)

    return response.text

llms/llama_llm.py (Local inference or llama.cpp-style)

def chat(prompt, temperature=0.7, max_tokens=300):

    # Placeholder for local inference logic

    return f"Local LLaMA says: {prompt}"

Step 4: Create the MCP Server

main.py

from fastapi import FastAPI, HTTPException

from models.schema import MCPRequest, MCPResponse

 

from llms import openai_llm, claude_llm, gemini_llm, llama_llm

 

app = FastAPI(title="MCP Server")

 

model_map = {

    "openai": openai_llm.chat,

    "claude": claude_llm.chat,

    "gemini": gemini_llm.chat,

    "llama": llama_llm.chat

}

 

@app.post("/mcp", response_model=MCPResponse)

def mcp_chat(request: MCPRequest):

    if request.model_provider not in model_map:

        raise HTTPException(status_code=400, detail="Invalid model provider")

 

    response = model_map[request.model_provider](

        prompt=request.prompt,

        temperature=request.temperature,

        max_tokens=request.max_tokens

    )

    return MCPResponse(response=response)

 

Step 5: Run the Server

uvicorn main:app --reload

Test using curl or Postman:

curl -X POST http://localhost:8000/mcp \

  -H "Content-Type: application/json" \

  -d '{"model_provider": "openai", "prompt": "Tell me a joke."}'

 

Real-Life Applications of MCP

1. Enterprise AI Gateway
   Use MCP to connect internal tools to any AI provider dynamically without rewriting code.
2. Model Benchmarking Tool
   Run the same prompt across multiple LLMs and compare results using one endpoint.
3. Failover Redundancy
   If one provider is down, MCP can switch to a backup model automatically.
4. A/B Testing Models
   Rotate models behind the scenes and gather performance feedback.
5. Education Platforms
   Let students experiment with multiple models via a single UI.

 

Advanced Extensions

• Authentication Layer: Add API key authentication to your server.
• Logging Middleware: Log prompts and responses for audit or training purposes.
• Load Balancing: Distribute load between providers based on quota or latency.
• WebSocket Support: Stream LLM responses live.

 

Conclusion

MCP simplifies and unifies communication across LLMs, making it easier to build scalable and flexible AI applications. With just one endpoint, you can access GPT, Claude, Gemini, LLaMA, and more—no vendor lock-in, no protocol juggling.

If you're looking for a complete setup including Docker deployment or a Streamlit-based interface for interactive testing, feel free to reach out—just send an email to business@ideagensys.com and we’ll share everything you need to get started.