MCP vs APIs, Agents SDK, A2A & More: Complete Comparison Guide

MCP vs Traditional Tool Calling, Plugins, and APIs

The Model Context Protocol (MCP) is fundamentally different from REST APIs, OpenAI function calling, LangChain tools, and ChatGPT plugins -- but it does not replace them. Instead, MCP provides a standardized AI-to-tool communication layer that works on top of existing approaches, unifying them into a single protocol any AI application can use.

This guide provides a detailed technical comparison of MCP against every major alternative, with code examples, feature tables, and guidance on when to use each approach.

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The Comparison at a Glance

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MCP vs REST APIs

What REST APIs Are

REST (Representational State Transfer) APIs are the dominant pattern for web service communication. They use HTTP methods (GET, POST, PUT, DELETE) to operate on resources identified by URLs.

# REST API: Create a GitHub issue
curl -X POST https://api.github.com/repos/owner/repo/issues \
  -H "Authorization: Bearer ghp_token" \
  -H "Content-Type: application/json" \
  -d '{"title": "Bug: Login timeout", "body": "Users report..."}'

What MCP Does Differently

An MCP server wraps this REST API and exposes it through the MCP protocol:

// MCP: Create a GitHub issue via tools/call
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_issue",
    "arguments": {
      "owner": "owner",
      "repo": "repo",
      "title": "Bug: Login timeout",
      "body": "Users report..."
    }
  }
}

Key Differences

1. Discovery: REST APIs require reading documentation. MCP servers declare their capabilities programmatically:

# MCP: AI discovers tools dynamically
tools = await session.list_tools()
# Returns structured list with names, descriptions, and parameter schemas

# REST: Developer reads docs, writes integration code manually
# No programmatic discovery mechanism

2. AI-optimized descriptions: MCP tool schemas include natural-language descriptions designed for AI comprehension:

{
  "name": "search_issues",
  "description": "Search GitHub issues by keyword, label, or state. Use this when the user wants to find existing issues. Returns issue number, title, state, and assignee.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "Search keywords to match against issue titles and bodies"
      },
      "state": {
        "type": "string",
        "enum": ["open", "closed", "all"],
        "description": "Filter by issue state"
      }
    },
    "required": ["query"]
  }
}

REST APIs have OpenAPI specs, but these are designed for developer documentation, not AI reasoning.

3. Stateful sessions: MCP maintains persistent connections with capability negotiation. REST APIs are stateless -- every request is independent.

4. Bidirectional communication: MCP servers can send notifications to clients (e.g., "the tool list changed"). REST APIs are strictly request-response.

5. Standardized error handling: MCP uses JSON-RPC error codes. REST APIs use HTTP status codes inconsistently across providers.

When to Use Each

Bottom line: REST APIs are for human developers building applications. MCP is for AI models accessing tools. They serve different purposes and work together -- MCP servers typically call REST APIs internally.

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MCP vs OpenAI Function Calling

What OpenAI Function Calling Is

OpenAI function calling (introduced June 2023) lets you define functions in your API request that GPT models can choose to invoke:

import openai

response = openai.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What's the weather in London?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"},
                    "units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["city"]
            }
        }
    }],
)

# Model returns a tool_call:
# {"name": "get_weather", "arguments": {"city": "London", "units": "celsius"}}
# Developer must execute the function and send result back manually

What MCP Does Differently

With MCP, the tool definitions come from the server, not from the API request:

# MCP approach: Tools are defined once in the server
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("weather-server")

@mcp.tool()
def get_weather(city: str, units: str = "celsius") -> str:
    """Get current weather for a city.

    Args:
        city: City name
        units: Temperature units (celsius or fahrenheit)
    """
    # Actual weather API call here
    return f"London: 15°C, partly cloudy"

# Server handles everything: discovery, execution, response formatting

Key Differences

1. Where tool definitions live:

• Function calling: Defined in each API request by the developer
• MCP: Defined once in the server, discovered dynamically by clients

2. Who executes the function:

• Function calling: The developer's code must catch the model's tool call, execute the function, and return the result
• MCP: The MCP client and server handle the entire execution loop

3. Vendor lock-in:

• Function calling: Only works with OpenAI models (or models that copied the format)
• MCP: Works with any AI model through any MCP-compatible host

4. Multi-model support:

• Function calling: Must redefine tools for each model provider's API format
• MCP: Define tools once in the server, works with all MCP clients

5. Additional capabilities:

• Function calling: Only supports function definitions
• MCP: Supports tools, resources, prompts, bidirectional notifications, and sampling

Code Comparison: Building a GitHub Integration

OpenAI Function Calling approach:

import openai
import requests

# Define tools in every API call
tools = [
    {
        "type": "function",
        "function": {
            "name": "list_issues",
            "description": "List GitHub issues",
            "parameters": {
                "type": "object",
                "properties": {
                    "repo": {"type": "string"},
                    "state": {"type": "string", "enum": ["open", "closed"]}
                },
                "required": ["repo"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "create_issue",
            "description": "Create a GitHub issue",
            "parameters": {
                "type": "object",
                "properties": {
                    "repo": {"type": "string"},
                    "title": {"type": "string"},
                    "body": {"type": "string"}
                },
                "required": ["repo", "title"]
            }
        }
    }
]

# Call the API with tools
response = openai.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
)

# Manually handle tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        if tool_call.function.name == "list_issues":
            args = json.loads(tool_call.function.arguments)
            result = requests.get(
                f"https://api.github.com/repos/{args['repo']}/issues",
                headers={"Authorization": f"Bearer {token}"},
                params={"state": args.get("state", "open")}
            ).json()
            # Format result, add to messages, call API again...

MCP approach:

# Server (build once, use with any AI app)
from mcp.server.fastmcp import FastMCP
import requests

mcp = FastMCP("github-server")
TOKEN = os.environ["GITHUB_TOKEN"]

@mcp.tool()
def list_issues(repo: str, state: str = "open") -> str:
    """List GitHub issues for a repository.

    Args:
        repo: Repository in 'owner/repo' format
        state: Issue state filter (open, closed, all)
    """
    response = requests.get(
        f"https://api.github.com/repos/{repo}/issues",
        headers={"Authorization": f"Bearer {TOKEN}"},
        params={"state": state}
    )
    issues = response.json()
    return "\n".join(f"#{i['number']}: {i['title']} ({i['state']})" for i in issues)

@mcp.tool()
def create_issue(repo: str, title: str, body: str = "") -> str:
    """Create a new GitHub issue.

    Args:
        repo: Repository in 'owner/repo' format
        title: Issue title
        body: Issue body/description
    """
    response = requests.post(
        f"https://api.github.com/repos/{repo}/issues",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={"title": title, "body": body}
    )
    issue = response.json()
    return f"Created issue #{issue['number']}: {issue['title']}"

# That's it. This server works with Claude, ChatGPT, Cursor, or any MCP client.

Interoperability

Importantly, MCP and function calling work together. When an MCP-compatible host application uses an OpenAI model, the host:

1. Discovers tools from connected MCP servers
2. Converts MCP tool schemas to OpenAI function calling format
3. Sends them with the API request
4. Catches the model's function calls
5. Routes them through MCP to the appropriate server
6. Returns results to the model

This means developers can use MCP for standardized tool management while models use function calling as their native invocation mechanism.

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MCP vs LangChain Tools

What LangChain Tools Are

LangChain is a popular Python/JavaScript framework for building LLM applications. It provides a Tool abstraction for defining functions that AI models can call:

from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent

@tool
def search_code(query: str, file_type: str = "") -> str:
    """Search for code patterns in the repository.

    Args:
        query: The search pattern
        file_type: Optional file extension filter
    """
    # Implementation
    return f"Found matches for '{query}'"

@tool
def read_file(path: str) -> str:
    """Read the contents of a file.

    Args:
        path: Path to the file
    """
    with open(path) as f:
        return f.read()

# Create an agent with these tools
llm = ChatOpenAI(model="gpt-4")
tools = [search_code, read_file]
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "Find the auth bug"})

Key Differences

1. Scope and portability: LangChain tools are Python/JS functions within your application. MCP servers are independent processes or services that any application can connect to.

2. Framework independence: LangChain tools require adopting the LangChain framework. MCP is framework-agnostic -- it works with LangChain, without LangChain, or with any other framework.

3. Language independence: LangChain tools must be written in Python or JavaScript. MCP servers can be written in any language that supports JSON-RPC.

4. Process isolation: MCP servers run as separate processes, providing security isolation, independent scaling, and the ability to use different languages for different tools.

LangChain + MCP Together

LangChain has added MCP integration, allowing LangChain applications to use MCP servers as tool providers:

from langchain_mcp import MCPToolkit

# Connect LangChain to an MCP server
toolkit = MCPToolkit(server_params={
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-github"],
    "env": {"GITHUB_PERSONAL_ACCESS_TOKEN": token}
})

# Get LangChain-compatible tools from the MCP server
tools = await toolkit.get_tools()

# Use them in a LangChain agent
agent = create_tool_calling_agent(llm, tools, prompt)

This demonstrates that MCP and LangChain are complementary. MCP provides the standardized server ecosystem; LangChain provides the agent framework.

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MCP vs ChatGPT Plugins

What ChatGPT Plugins Were

ChatGPT plugins (launched March 2023, later deprecated) allowed third-party developers to extend ChatGPT with external capabilities:

# Plugin manifest (ai-plugin.json)
{
  "schema_version": "v1",
  "name_for_human": "Weather Plugin",
  "name_for_model": "weather",
  "description_for_human": "Get weather forecasts",
  "description_for_model": "Get current weather data for any city",
  "auth": { "type": "none" },
  "api": {
    "type": "openapi",
    "url": "https://weather-plugin.example.com/openapi.yaml"
  }
}

Plugins used OpenAPI specifications to describe their HTTP APIs, and ChatGPT would call these APIs based on user requests.

Why Plugins Failed and MCP Succeeded

ChatGPT plugins failed for several reasons that MCP's design explicitly addresses:

1. Single platform: Plugins only worked with ChatGPT. MCP works with everything.
2. High barrier to entry: Plugins required deploying a web service with an OpenAPI spec. MCP servers can be a single file.
3. No local tools: Plugins could not access local files, databases, or tools. MCP's stdio transport enables this.
4. Centralized control: OpenAI controlled the marketplace and could reject plugins. MCP is open and permissionless.

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MCP vs Google Gemini Extensions

What Gemini Extensions Are

Google's Gemini platform provides Extensions -- built-in integrations that connect Gemini to Google services and third-party APIs:

# Google Gemini extension usage (conceptual)
import google.generativeai as genai

model = genai.GenerativeModel(
    "gemini-pro",
    tools=[
        genai.Tool(google_search_retrieval={}),
        genai.Tool(code_execution={}),
    ]
)

response = model.generate_content("Search for MCP protocol news")

Key Differences

Gemini extensions provide tight integration with Google's ecosystem (Search, Maps, Code Execution) but lack the openness and flexibility of MCP. Google's MCP support announcement signals recognition that a universal standard is more valuable than a proprietary one.

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MCP vs AWS Bedrock Tool Use

What Bedrock Tool Use Is

AWS Bedrock provides a tool-use API similar to OpenAI's function calling, but for models hosted on the Bedrock platform:

import boto3

client = boto3.client("bedrock-runtime")

response = client.converse(
    modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
    messages=messages,
    toolConfig={
        "tools": [{
            "toolSpec": {
                "name": "get_weather",
                "description": "Get weather for a city",
                "inputSchema": {
                    "json": {
                        "type": "object",
                        "properties": {
                            "city": {"type": "string"}
                        },
                        "required": ["city"]
                    }
                }
            }
        }]
    }
)

Key Differences

Like OpenAI function calling, Bedrock tool use defines tools per-request and requires the developer to handle execution. MCP centralizes tool definitions in servers that work across all platforms.

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Detailed Feature Comparison

Tool Definition and Discovery

Execution Model

Security Model

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When to Use Each Approach

Use REST APIs When:

• Building web or mobile applications for human users
• Creating service-to-service integrations without AI
• You need the broadest compatibility with existing tooling
• The consumer is a developer, not an AI model

Use OpenAI Function Calling When:

• Building a single-model application using only OpenAI models
• You have a small number of tools (under 10)
• You want the simplest possible integration with GPT models
• You do not need cross-model compatibility

Use LangChain Tools When:

• Building a Python application with LangChain as your framework
• You want rapid prototyping with pre-built LangChain tools
• Your tools are purely in-process Python functions
• You are comfortable with framework lock-in

Use MCP When:

• You need tools that work across multiple AI applications
• You want to share tools between Claude, ChatGPT, Cursor, and other hosts
• You need local tool access (filesystem, databases, local services)
• You are building for the long term and want vendor independence
• You want to benefit from the growing MCP ecosystem
• You need stateful, bidirectional communication
• You need standardized security (OAuth 2.1, permissions, consent)
• You are building agent workflows that orchestrate multiple tools

Decision Matrix

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Migration Path: Moving to MCP

From OpenAI Function Calling

If you have existing function calling implementations, wrapping them as MCP servers is straightforward:

# Before: OpenAI function calling
tools_for_openai = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get weather for a city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["city"]
        }
    }
}]

def handle_get_weather(city: str, units: str = "celsius") -> str:
    # Your existing implementation
    return fetch_weather(city, units)

# After: MCP server (wraps the same logic)
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("weather-server")

@mcp.tool()
def get_weather(city: str, units: str = "celsius") -> str:
    """Get weather for a city.

    Args:
        city: City name
        units: Temperature units (celsius or fahrenheit)
    """
    return fetch_weather(city, units)  # Same implementation

if __name__ == "__main__":
    mcp.run()

From LangChain Tools

# Before: LangChain tool
from langchain.tools import tool

@tool
def search_docs(query: str) -> str:
    """Search documentation by keyword."""
    return perform_search(query)

# After: MCP server
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("docs-server")

@mcp.tool()
def search_docs(query: str) -> str:
    """Search documentation by keyword.

    Args:
        query: Search keywords
    """
    return perform_search(query)  # Same implementation

From REST API Wrappers

# Before: Direct REST API integration in your app
import requests

def search_github_issues(repo, query):
    response = requests.get(
        f"https://api.github.com/search/issues",
        params={"q": f"{query} repo:{repo}"},
        headers={"Authorization": f"Bearer {token}"}
    )
    return response.json()

# After: MCP server wrapping the same API
from mcp.server.fastmcp import FastMCP
import requests

mcp = FastMCP("github-search")

@mcp.tool()
def search_issues(repo: str, query: str) -> str:
    """Search GitHub issues in a repository.

    Args:
        repo: Repository in 'owner/repo' format
        query: Search keywords
    """
    response = requests.get(
        f"https://api.github.com/search/issues",
        params={"q": f"{query} repo:{repo}"},
        headers={"Authorization": f"Bearer {os.environ['GITHUB_TOKEN']}"}
    )
    issues = response.json()["items"]
    return "\n".join(f"#{i['number']}: {i['title']}" for i in issues[:10])

The pattern is consistent: take your existing tool implementation, wrap it in an MCP server, and it becomes accessible to the entire AI ecosystem.

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MCP vs Google Agent-to-Agent (A2A) Protocol

Google introduced the Agent-to-Agent (A2A) protocol in April 2025 as a standard for communication between AI agents themselves -- not between agents and tools. Understanding the distinction is critical.

Different Layers, Different Problems

MCP and A2A operate at fundamentally different layers of the AI stack:

When to Use Each

Use MCP when an AI application needs to interact with tools, databases, APIs, or external systems. MCP is the "hands" of the AI -- it reaches out and does things.

Use A2A when you have multiple specialized AI agents that need to collaborate on complex tasks. A2A is the "conversation" between agents -- they negotiate, delegate, and report back.

Use both together in production agent systems. A multi-agent system might use A2A for inter-agent communication while each individual agent uses MCP to access the tools it needs. They are complementary, not competing.

Architecture Example

A typical enterprise system might look like this:

Customer Support Agent (uses MCP for CRM tools)
        ↕ A2A
Billing Agent (uses MCP for payment tools)
        ↕ A2A
Shipping Agent (uses MCP for logistics tools)

Each agent uses MCP to access its domain-specific tools, and A2A to communicate with other agents.

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MCP vs OpenAI Agents SDK

OpenAI released the Agents SDK (formerly Swarm) in March 2025 to provide a framework for building multi-agent systems. OpenAI also added native MCP support in the same release, but the Agents SDK has its own tool calling patterns.

Framework vs Protocol

The key difference is scope: the OpenAI Agents SDK is a framework for building agent applications on OpenAI models, while MCP is a protocol for connecting any AI model to any tool.

The Convergence

Notably, OpenAI added MCP support directly into the Agents SDK. You can use MCP servers as tool providers within OpenAI agents. This means MCP and the Agents SDK are not mutually exclusive -- MCP provides the tool layer while the Agents SDK provides the agent orchestration layer.

Recommendation

If you are building exclusively with OpenAI models and want rapid development, the Agents SDK with MCP tool servers gives you the best of both worlds. If you need model portability, build your tool layer on MCP and your agent layer on a model-agnostic framework.

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MCP vs Microsoft Semantic Kernel

Microsoft Semantic Kernel is an SDK for building AI-powered applications, with its own plugin system for connecting AI to external functions. It competes with LangChain at the application framework layer.

Plugin System vs Protocol

Semantic Kernel uses "plugins" (collections of functions) that are registered directly in application code. MCP uses servers that run as separate processes and communicate over a standard protocol.

Using Both Together

Semantic Kernel added MCP integration, allowing Kernel applications to use MCP servers as plugin providers. This is the recommended approach for .NET teams: use Semantic Kernel as your application framework and MCP for tool integration. You get the enterprise features of Semantic Kernel (planners, memory, RAG) with the universal tool ecosystem of MCP.

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The Modern Landscape: How Everything Fits Together

The AI tool integration landscape in 2026 has settled into clear layers:

MCP sits at the tool protocol layer and integrates with everything above it. This is its strategic advantage -- you build your MCP server once, and it works with LangChain, Semantic Kernel, the Agents SDK, and any future framework.

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The Convergence Trend

Everyone Is Adopting MCP

The competitive landscape of AI tool integration is converging on MCP:

• OpenAI: Added MCP support alongside its native function calling
• Google: Signaled MCP compatibility for Gemini
• Microsoft: MCP support in VS Code Copilot and GitHub Copilot
• LangChain: Added MCP integration so LangChain agents can use MCP servers
• LlamaIndex: MCP server support for RAG workflows
• Every major AI IDE: Cursor, Windsurf, Zed all support MCP

This convergence is not accidental. The industry has recognized that proprietary tool integration does not scale, and MCP provides the open standard everyone needs.

What This Means for Your Architecture

The convergence suggests a clear architectural direction:

1. Build MCP servers for your tools and services
2. Use your preferred AI framework (LangChain, LlamaIndex, custom) as the application layer
3. Connect to any AI model through MCP-compatible hosts
4. Benefit from the ecosystem as new servers and clients appear

This architecture gives you maximum flexibility, minimum vendor lock-in, and access to the broadest possible ecosystem of tools.

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Summary

MCP is not a replacement for REST APIs, function calling, or frameworks like LangChain. It is a complementary protocol that standardizes how AI applications discover and use tools, regardless of the underlying implementation.

The key insight is that MCP operates at a different level of the stack. REST APIs serve human developers. Function calling serves specific AI platforms. Frameworks serve specific language ecosystems. MCP serves the entire AI ecosystem, providing a universal bridge between any AI application and any tool.

For most AI projects in 2026, the answer is clear: use MCP for tool integration, use your preferred framework for application logic, and use existing APIs inside your MCP servers.

Learn more:

• What is MCP? -- The complete protocol guide
• MCP Architecture -- How the protocol works under the hood
• MCP Building Blocks -- Deep dive into tools, resources, and prompts
• Browse MCP Servers -- Explore the ecosystem