Exploring LangChain MCP Features with Sample Code

Key LangChain MCP Features

• MCP Agent Integration: LangChain allows you to create agents that can communicate with MCP servers. These agents can be equipped with tools defined on the MCP server.
• Tool Abstraction: LangChain provides an abstraction over MCP tools, making them accessible within the agent framework as standard LangChain tools.
• Structured Communication: LangChain handles the structured communication between the LLM and the MCP server, including formatting requests and parsing responses.
• State Management: For more complex interactions, LangChain can help manage the state of the conversation and the interactions with the MCP server.

Sample Code: Connecting to an MCP Server and Using a Tool

This example demonstrates how to connect a LangChain agent to a hypothetical MCP server and use a tool called “get_weather” that takes a “location” as input.

import os
from langchain.agents import AgentType, initialize_agent
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.mcp import MCPServerTool

# Replace with your actual Anthropic API key and MCP server details
os.environ["ANTHROPIC_API_KEY"] = "YOUR_ANTHROPIC_API_KEY"
mcp_server_url = "http://your-mcp-server.com:8080"

# Initialize the LLM (Claude is commonly used with MCP)
llm = ChatAnthropic(model_name="claude-3-opus-20240229")

# Define the MCP tools to be used by the agent
tools = [
    MCPServerTool.from_url(
        url=mcp_server_url,
        tool_description="Use this tool to get the current weather information for a given location.",
        tool_name="get_weather"
    )
]

# Initialize the agent
agent = initialize_agent(
    llm=llm,
    tools=tools,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
)

# Run the agent with a user query
user_query = "What is the weather like in Bentonville, Arkansas?"
response = agent.run(user_query)

print(response)

Explanation:

• We import the necessary modules from LangChain and the Anthropic integration.
• We set the Anthropic API key and the URL of our MCP server.
• We initialize the Claude LLM, which is often used with MCP due to Anthropic’s development of the protocol.
• We define a list of tools. Here, we use `MCPServerTool.from_url` to create a LangChain tool that wraps the “get_weather” tool available on the specified MCP server. We provide a description and a name for the tool.
• We initialize a LangChain agent using the LLM and the defined tools. `ZERO_SHOT_REACT_DESCRIPTION` is a common agent type that decides which tool to use based on the tool descriptions.
• We then run the agent with a user query. The agent will analyze the query, recognize that it needs weather information, and use the “get_weather” tool (which communicates with the MCP server) to retrieve the answer.
• Finally, we print the agent’s response.

Sample Code: Interacting with a Tool Requiring Multiple Inputs

This example shows how to interact with an MCP tool that requires multiple inputs, such as a “schedule_meeting” tool that needs “attendees” and “time”.

import os
from langchain.agents import AgentType, initialize_agent
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.mcp import MCPServerTool

# Replace with your actual Anthropic API key and MCP server details
os.environ["ANTHROPIC_API_KEY"] = "YOUR_ANTHROPIC_API_KEY"
mcp_server_url = "http://your-mcp-server.com:8080"

# Initialize the LLM
llm = ChatAnthropic(model_name="claude-3-opus-20240229")

# Define the MCP tools
tools = [
    MCPServerTool.from_url(
        url=mcp_server_url,
        tool_description="Use this tool to schedule a meeting with specified attendees at a given time.",
        tool_name="schedule_meeting"
    )
]

# Initialize the agent
agent = initialize_agent(
    llm=llm,
    tools=tools,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
)

# Run the agent with a user query requiring multiple inputs
user_query = "Schedule a meeting with John and Jane tomorrow at 10 AM."
response = agent.run(user_query)

print(response)

Explanation:

• The setup is similar to the previous example.
• The “schedule_meeting” tool on the MCP server is expected to handle the extraction of “attendees” (John and Jane) and “time” (tomorrow at 10 AM) from the user query. The LangChain agent will pass the relevant information to the MCP tool.
• The MCP server and the “schedule_meeting” tool would need to be implemented to correctly interpret these inputs and perform the scheduling action.

Key Considerations

• MCP Server Implementation: The functionality relies heavily on the tools defined and implemented on the MCP server. LangChain acts as the client to interact with this server.
• Tool Descriptions: Clear and concise tool descriptions are crucial for the LangChain agent to understand when and how to use each MCP tool.
• Error Handling: Robust error handling should be implemented on both the LangChain side and the MCP server side to manage potential issues during communication and tool execution.
• Security: Secure communication between the LangChain application and the MCP server is essential, especially when dealing with sensitive data or actions.

Further Learning

• LangChain MCP Documentation: LangChain MCP Agents Documentation
• Langchain MCP Adapter Blog Post: Langchain MCP Adapter: A Step-by-Step Guide to Build MCP Agents
• Analytics Vidhya Article on LangChain MCP: MCP Client-Server using Langchain
• Anthropic Documentation on MCP: Refer to Anthropic’s official documentation for the Model Context Protocol for a deeper understanding of the underlying technology.