Complete MCP Protocol Guide: 12 Core AI Agent Development Frameworks Deep Analysis

AI Tech Blog9/3/25...About 6 min

Complete MCP Protocol Guide: 12 Core AI Agent Development Frameworks Deep Analysis

With the rapid development of Large Language Models (LLMs) and generative AI, AI Agents are becoming the hottest technology trend of 2025. These agents have transcended the limitations of traditional chatbots, possessing powerful capabilities to understand complex instructions, autonomously plan decisions, and interact with external systems.

However, AI agent development faces a core challenge: How to provide standardized tool calling and system integration methods for agents?

MCP (Model Context Protocol) is the key technology to solve this problem.

MCP aims to provide a universal specification and structured format for AI agents to interact with external environments (such as tools, databases, APIs). It's like the "HTTP protocol" for AI agents, ensuring that agents and tools built by different developers can seamlessly collaborate, greatly improving development efficiency and system interoperability.

To help developers get started quickly, a thriving MCP ecosystem has formed. This article will take you through 12 core MCP development frameworks and SDKs with code examples to help you choose the most suitable tools for your project.

Core SDKs & Official Implementations

These are fundamental tools for implementing MCP specifications, suitable for developers building MCP-compatible services from scratch.

1. OpenAI SDK

As an industry leader, OpenAI's official SDK has built-in support for MCP, allowing developers to easily integrate MCP-compliant tools into their AI applications.

Explanation: This means you can directly provide tool sets through a standard MCP endpoint when calling OpenAI models, without manually handling complex tool calling logic.

Code Example (Conceptual Python example):

from openai import OpenAI
client = OpenAI()

# Directly use MCP server URL as tool source
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Help me check today's weather in San Francisco and send an email summary"}],
    # tool_source points to an MCP-compliant tool server
    tool_source={"type": "mcp", "url": "https://api.my-tools.com/mcp/v1"}
)

print(response.choices[0].message.content)

2. MCP Python SDK

The official Python SDK launched by MCP is the preferred tool for building MCP-compatible servers, providing standardized implementation methods.

Explanation: If you want to create a tool server in Python and let any MCP-supporting agent (like OpenAI Agent) call it, this SDK is your best choice.

Code Example (FastAPI style):

from mcp_sdk import create_app, tool
import uvicorn

@tool
def get_weather(city: str) -> str:
    """Get current weather for the specified city."""
    # In real scenarios, would call weather API
    return f"The weather in {city} is sunny, 25°C."

# create_app automatically converts functions with @tool decorator to MCP-compatible endpoints
app = create_app(tools=[get_weather], title="My Weather Tool Server")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

3. MCP TypeScript SDK

Corresponding to the Python SDK, this is the official TypeScript toolkit for building MCP-compatible servers based on official schemas.

Explanation: Perfect for developers working in Node.js or Deno environments, especially teams building full-stack TypeScript applications.

Code Example (Express style):

import { createMcpRouter, tool } from '@mcp-sdk/typescript';
import express from 'express';

const getStockPrice = tool({
  name: 'getStockPrice',
  description: 'Get current price for the specified stock symbol.',
  input: { symbol: { type: 'string' } },
  handler: async ({ symbol }) => {
    // Call stock API
    return { price: 250.75 };
  },
});

const app = express();
// Mount MCP router to /mcp path
app.use('/mcp', createMcpRouter({ tools: [getStockPrice] }));

app.listen(3000, () => console.log('MCP Server running on port 3000'));

Full-Stack Agent Building Frameworks

These are more comprehensive frameworks that not only support MCP but also provide advanced features like task coordination and workflow orchestration.

4. LastMile MCP Agent

A workflow-driven framework for creating MCP-compatible agents with complex task coordination logic.

Explanation: When you need to build agents that don't just call single tools but execute a series of interdependent steps (e.g., search information -> analyze data -> generate report), LastMile becomes very useful.

Code Example (Conceptual YAML definition):

# Define an agent workflow
agent:
  name: research_and_report_agent
  protocol: mcp # Specify using MCP protocol

workflow:
  - step: search
    tool: web_search
    input: "AI agents market size 2025"
  - step: analyze
    tool: data_analysis
    input: steps.search.output
    prompt: "Summarize the key findings from the search results."
  - step: write_report
    tool: text_generator
    input: steps.analyze.output
    prompt: "Write a short report based on the summary."

5. Strands Agents

A model-driven SDK for designing, deploying, and orchestrating MCP agents with typed schemas.

Explanation: Strands emphasizes type safety, ensuring reliable and predictable interactions between agents and tools through strict schema definitions, reducing runtime errors.

Code Example (Conceptual Python example):

from strands import Agent, Strand, mcp_server
from pydantic import BaseModel

# Use Pydantic to define strict input/output types
class WeatherInput(BaseModel):
    city: str
    unit: str = "celsius"

class WeatherOutput(BaseModel):
    temperature: float
    description: str

# Define a "Strand", an executable task unit
weather_strand = Strand(
    name="get_weather",
    input_schema=WeatherInput,
    output_schema=WeatherOutput,
    handler=lambda i: WeatherOutput(temperature=25.0, description="Sunny")
)

# Create an Agent with this Strand and expose via MCP
agent = Agent(strands=[weather_strand])
server = mcp_server(agent)

6. Praison AI

A Python-based multi-agent framework specifically for building and running MCP-aligned agents and servers.

Explanation: Praison AI's specialty is multi-agent collaboration. You can create multiple specialized agents (like a researcher, a writer) that communicate and collaborate through MCP protocol to complete complex tasks together.

Code Example (Conceptual Python example):

from praisonai import PraisonAI, Agent

# Define researcher agent
researcher = Agent(
    role="Researcher",
    goal="Find the latest news about MCP protocol",
    # Tools can be obtained from external sources via MCP protocol
    mcp_tool_server="http://my-tools/mcp"
)

# Define writer agent
writer = Agent(
    role="Writer",
    goal="Write a blog post based on the researcher's findings"
)

# Create an agent team to execute tasks
crew = PraisonAI(agents=[researcher, writer])
result = crew.run("Write a blog post about the latest MCP news.")

Integration & Adapter Layers

These tools are designed to connect existing AI frameworks or applications with the MCP ecosystem.

7. Langchain MCP Adapter

A lightweight wrapper for connecting the popular LangChain/LangGraph with MCP-based tool chains.

Explanation: If you're already building applications with LangChain but want to leverage specific tool sets from the MCP ecosystem, this adapter helps you easily bridge them without rewriting your entire application.

Code Example (Conceptual Python example):

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_mcp_adapter import McpToolSource

# Create a tool source pointing to MCP server
mcp_tools = McpToolSource(url="https://api.my-tools.com/mcp/v1").fetch_tools()

llm = ChatOpenAI(model="gpt-4o")
prompt = ... # Your agent prompt

# Use tools fetched from MCP server directly in LangChain Agent
agent = create_tool_calling_agent(llm, mcp_tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=mcp_tools)

agent_executor.invoke({"input": "What's the weather in London?"})

8. Google ADK (Agent Development Kit)

Google's open-source agent development kit with native support for MCP servers.

Explanation: ADK provides a comprehensive solution to help developers build, deploy, and manage agents in the Google Cloud ecosystem. Its native MCP support means you can easily integrate external MCP tools into Google technology-based agents.

9. Composio

An SDK for integrating OpenAI agents with Composio-managed, MCP-compatible servers and workflows.

Explanation: Composio provides a managed tool platform. You can connect and manage hundreds of SaaS applications (like Slack, Jira, HubSpot) through its interface, and Composio automatically packages them into an MCP-compatible server for your agents to call.

Code Example (Conceptual Python example):

from openai import OpenAI
from composio import Composio

client = OpenAI()
composio = Composio(api_key="COMPOSIO_API_KEY")

# Composio provides a unique MCP URL containing all your connected tools
mcp_tool_server_url = composio.get_mcp_server_url()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Send a message in Slack's #general channel: 'Hello from my AI agent!'"}],
    tool_source={"type": "mcp", "url": mcp_tool_server_url}
)

10. Semantic Kernel

Microsoft's orchestration SDK for integrating tools and AI agents in serverless environments.

Explanation: Semantic Kernel (SK) focuses on binding LLMs with existing code and business logic (through "plugins"). Its MCP support means SK-created "plugins" can be exposed as MCP tools, or SK agents can call external MCP tools, particularly suitable for enterprise .NET and Python environments.

Frontend & Serverless Integration

These SDKs focus on connecting frontend applications or serverless functions with MCP backends.

11. CopilotKit MCP Support

Enables frontend applications to directly communicate with any compatible MCP server and provides built-in integration support.

Explanation: This is a frontend-first solution. If you want to build a chat interface in your React or Vue application that allows users to directly interact with backend MCP agents, CopilotKit provides corresponding UI components and hooks, greatly simplifying frontend development.

Code Example (Conceptual React/JSX example):

import { useCopilotAction } from "@copilotkit/react-core";
import { CopilotTask } from "@copilotkit/react-ui";

// Frontend directly defines an Action that can be called by backend AI
useCopilotAction({
  name: "sayHello",
  description: "Says hello to the user.",
  handler: async ({ name }) => {
    alert(`Hello, ${name}!`);
  },
});

// CopilotKit connects frontend Actions with backend MCP Agent
function App() {
  // Tasks are planned by backend MCP Agent and may call frontend Actions
  const task = new CopilotTask({
    initialMessages: ...
    // Points to your MCP-compatible backend
    mcp_endpoint: "/api/mcp-agent"
  });
  return <YourChatUI task={task} />;
}

12. Vercel AI SDK

A client SDK with MCP support for connecting applications deployed on Vercel platform with tools and AI agents.

Explanation: Optimized specifically for the Vercel ecosystem. It provides easy-to-use React hooks and helper functions for Svelte/Vue, allowing you to easily perform streaming interactions with MCP agents in serverless functions or edge functions.

Code Example (Conceptual React/Next.js example):

import { useChat } from 'ai/react';

export default function Chat() {
  // useChat hook can be configured to communicate with a backend API route implementing MCP protocol
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: '/api/chat-mcp', // This API route is backed by an MCP agent
  });

  return (
    <div>
      {/* Render chat interface */}
    </div>
  );
}

Conclusion

The MCP protocol and its rich ecosystem are paving the way for building next-generation AI agents. By providing a unified communication standard, it breaks down barriers between different platforms and frameworks, allowing developers to freely combine tools and models from different sources like assembling LEGO blocks.

Whether you want to build a powerful tool server from scratch, connect existing LangChain applications to a broader ecosystem, or integrate agent capabilities into frontend applications, there's always a tool in this ecosystem that suits you. Now, choose a framework that interests you and start building your first MCP agent! 🚀