Connect AI models with tools and external systems using Anthropic's open protocol. Enable powerful tool use capabilities in your AI applications with standardized integration.
MCP Architecture: Connecting AI models with tools and services
The Model Context Protocol (MCP) is an open specification that enables AI models to interact with external tools and services, expanding their capabilities beyond text generation.
Provides a consistent way for AI models to connect with various tools and services, reducing integration complexity.
Works with any tool or service that implements the MCP specification, providing flexibility in your tech stack.
Includes built-in security features to ensure safe interactions between AI models and external tools.
How the Model Context Protocol enables AI models to interact with tools
The large language model that processes inputs and generates responses, deciding when to use tools.
External services and functions that the model can call to perform specific tasks.
Manages the communication between the AI model and the available tools.
User sends a query or request to the AI system.
AI model determines if a tool is needed and selects the appropriate one.
MCP Connector routes the request to the selected tool and executes it.
AI model processes the tool's output and generates a final response.
Clear separation of concerns makes unit testing and integration testing more straightforward.
Each component has a single responsibility, making the codebase easier to maintain and update.
Components can be scaled independently based on the application's needs.
Our experts can help you design and implement a robust MCP architecture for your application.
Get in TouchGet started with implementing the Model Context Protocol in your projects
First, install the MCP server package:
# Install the MCP server
pip install mcp-server
# Start the server
mcp-server start --port 8000
This will start the MCP server on port 8000. The server will handle tool registration and execution.
Create a Python script to define and register your tools:
from mcp import MCPClient, Tool
# Initialize the MCP client
client = MCPClient("http://localhost:8000")
# Define a web search tool
@Tool("web_search", "Search the web for information")
async def web_search(query: str, max_results: int = 5):
"""Search the web for the given query."""
# Implementation here
return search_results
# Register the tool
client.register_tool(web_search)
Configure your AI model to use the MCP server for tool execution:
import anthropic
# Initialize the Anthropic client
client = anthropic.Client(api_key="your-api-key")
# Make a request with tool use
response = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1000,
tools=[{"type": "web_search", "name": "web_search"}],
messages=[
{"role": "user", "content": "What's the latest news about AI?"}
]
)
print(response.content)
Tools should return data in a structured format:
{
"status": "success",
"data": [
{
"title": "Latest AI Breakthrough",
"source": "example.com",
"snippet": "Researchers have made significant progress..."
}
]
}
Here's a complete example of implementing a web search tool with MCP:
import requests
from mcp import Tool, MCPClient
class WebSearchTool:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = "https://api.searchprovider.com/v1/search"
@Tool("web_search", "Search the web for information")
async def search(self, query: str, max_results: int = 5):
"""
Search the web for the given query.
Args:
query: The search query
max_results: Maximum number of results to return (1-10)
"""
params = {
"q": query,
"limit": min(max(1, max_results), 10),
"api_key": self.api_key
}
try:
response = requests.get(self.base_url, params=params)
response.raise_for_status()
return {
"status": "success",
"data": response.json()["results"][:max_results]
}
except Exception as e:
return {
"status": "error",
"message": str(e)
}
# Initialize and register the tool
search_tool = WebSearchTool(api_key="your-api-key")
client = MCPClient("http://localhost:8000")
client.register_tool(search_tool.search)
Why choose the Model Context Protocol for your AI applications
Extend your AI models with specialized tools for tasks like web search, calculations, and data analysis, going beyond basic text generation.
Maintain control over which tools your AI can access and how they're used, with built-in security features and access controls.
Easily add, remove, or update tools without modifying your core AI model, enabling rapid iteration and experimentation.
Offload complex computations to specialized tools, reducing latency and improving the responsiveness of your AI applications.
Enable your AI to access and process real-time data from external sources, keeping responses current and relevant.
MCP works with multiple AI providers and can be integrated with various tools and services, avoiding vendor lock-in.