MCP

Integrating MCP Servers with Popular AI Frameworks

By Lisa Wang•6/10/2024•9 min read
Integration

MCP servers can significantly enhance the capabilities of popular AI frameworks by providing access to external tools and data sources. This guide covers integration patterns for the most widely used AI frameworks.

LangChain Integration

Setting Up MCP with LangChain

LangChain provides excellent support for MCP servers through its built-in MCP integration capabilities.

from langchain.agents import initialize_agent
from langchain.mcp import MCPToolkit
from langchain.llms import OpenAI

# Initialize MCP toolkit
mcp_toolkit = MCPToolkit(
    server_url="http://localhost:8000",
    tools=["file_reader", "data_processor"]
)

# Create agent with MCP tools
agent = initialize_agent(
    tools=mcp_toolkit.get_tools(),
    llm=OpenAI(),
    agent="zero-shot-react-description",
    verbose=True
)

# Use the agent
response = agent.run("Read the file 'data.csv' and analyze its contents")
print(response)

Custom MCP Tools

You can also create custom MCP tools specifically for LangChain:

from langchain.tools import BaseTool
from typing import Optional

class MCPFileTool(BaseTool):
    name = "mcp_file_reader"
    description = "Read files using MCP server"
    
    def _run(self, file_path: str) -> str:
        # Implement MCP server call
        return "File contents"
    
    async def _arun(self, file_path: str) -> str:
        # Async implementation
        return await self._run(file_path)

OpenAI Function Calling

MCP as OpenAI Functions

You can expose MCP server capabilities as OpenAI function calls:

import openai
from typing import List, Dict

def get_mcp_functions():
    return [
        {
            "name": "read_file",
            "description": "Read contents of a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "file_path": {
                        "type": "string",
                        "description": "Path to the file to read"
                    }
                },
                "required": ["file_path"]
            }
        }
    ]

def call_mcp_function(name: str, arguments: Dict):
    # Implement MCP server call
    if name == "read_file":
        return read_file_from_mcp(arguments["file_path"])
    return None

# Use with OpenAI
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Read the file 'report.txt'"}],
    functions=get_mcp_functions(),
    function_call="auto"
)

Anthropic Claude Integration

Using Claude with MCP

Claude can work with MCP servers through its tool use capabilities:

import anthropic

client = anthropic.Anthropic()

def create_mcp_tools():
    return [
        {
            "name": "file_operations",
            "description": "Perform file operations",
            "input_schema": {
                "type": "object",
                "properties": {
                    "operation": {"type": "string", "enum": ["read", "write", "delete"]},
                    "file_path": {"type": "string"}
                }
            }
        }
    ]

message = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1024,
    tools=create_mcp_tools(),
    messages=[
        {
            "role": "user",
            "content": "Read the file 'data.json' and summarize its contents"
        }
    ]
)

Hugging Face Transformers

Custom Pipeline with MCP

Integrate MCP servers with Hugging Face pipelines:

from transformers import pipeline
import requests

class MCPEnhancedPipeline:
    def __init__(self, mcp_server_url: str):
        self.mcp_url = mcp_server_url
        self.classifier = pipeline("text-classification")
    
    def classify_with_context(self, text: str, context_file: str):
        # Get context from MCP server
        context = self.get_context_from_mcp(context_file)
        
        # Combine text with context
        enhanced_text = f"{context}

{text}"
        
        # Perform classification
        return self.classifier(enhanced_text)
    
    def get_context_from_mcp(self, file_path: str):
        response = requests.post(f"{self.mcp_url}/tools/call", json={
            "name": "read_file",
            "arguments": {"file_path": file_path}
        })
        return response.json()["content"]

# Usage
pipeline = MCPEnhancedPipeline("http://localhost:8000")
result = pipeline.classify_with_context(
    "This is a positive review",
    "context/reviews.txt"
)

Vector Database Integration

MCP with Vector Stores

Combine MCP servers with vector databases for enhanced retrieval:

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.mcp import MCPToolkit

class MCPVectorStore:
    def __init__(self, mcp_server_url: str):
        self.mcp_toolkit = MCPToolkit(server_url=mcp_server_url)
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = None
    
    def load_documents_from_mcp(self, file_paths: List[str]):
        documents = []
        for file_path in file_paths:
            content = self.mcp_toolkit.call_tool("read_file", {"file_path": file_path})
            documents.append(content)
        
        self.vectorstore = Chroma.from_texts(
            documents,
            self.embeddings
        )
    
    def similarity_search(self, query: str, k: int = 4):
        return self.vectorstore.similarity_search(query, k=k)

# Usage
store = MCPVectorStore("http://localhost:8000")
store.load_documents_from_mcp(["doc1.txt", "doc2.txt", "doc3.txt"])
results = store.similarity_search("What is MCP?")

Best Practices

Error Handling

  • Implement robust error handling for MCP calls
  • Provide fallback mechanisms
  • Log all MCP interactions
  • Handle timeouts gracefully

Performance Optimization

  • Cache frequently used MCP responses
  • Use connection pooling
  • Implement request batching
  • Monitor MCP server performance

Security Considerations

  • Validate all inputs to MCP servers
  • Implement proper authentication
  • Use secure communication channels
  • Monitor for suspicious activities

Testing and Debugging

Integration Testing

  • Test MCP server connectivity
  • Verify tool availability
  • Test error scenarios
  • Validate response formats

Debugging Tools

  • Use MCP server logs
  • Implement request/response logging
  • Use debugging proxies
  • Monitor performance metrics

Conclusion

Integrating MCP servers with popular AI frameworks opens up powerful possibilities for building sophisticated AI applications. By following these patterns and best practices, you can create robust, scalable, and secure integrations that enhance your AI capabilities significantly.

Remember to start simple and gradually add complexity as you become more familiar with both the AI frameworks and MCP server capabilities.

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