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MCP-enabled tool for retrieving information from LangGraph documentation

Core Idea: The LangGraph Query Tool is an MCP tool implementation that provides AI applications with the ability to search and retrieve relevant information from LangGraph documentation, enabling more accurate responses about LangGraph concepts and usage.

Key Elements

Implementation Details

• Vector Store Backend: Uses embeddings to create a searchable index of LangGraph documentation

• Document Processing:

  • Splits documents into chunks (typically 8,000 tokens to match embedding model context)
  • Embeds document chunks using models like OpenAI embeddings
  • Creates a vector store for semantic similarity search

• Query Processing:

  • Takes natural language queries as input
  • Converts queries to embeddings using the same model
  • Performs similarity search against the document index
  • Returns the most relevant document chunks

Technical Implementation

from langchain_core import tool
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

@tool
def langgraph_query_tool(query: str) -> str:
    """Search LangGraph documentation for information about the given query."""
    # Load the vector store
    vector_store = Chroma(
        embedding_function=OpenAIEmbeddings(),
        persist_directory="./langgraph_docs_store"
    )
    
    # Perform search
    docs = vector_store.similarity_search(query, k=3)
    
    # Format results
    results = "\n\n".join([doc.page_content for doc in docs])
    return results

MCP Integration

• Server Registration:

  from model_context_protocol.fast_mcp import FastMCP
  
  server = FastMCP()
  server.add_tool("langgraph_query_tool", langgraph_query_tool)
  server.run(transport="stdio")
  

• Host Application Usage:

  • Tool appears in the list of available tools in host applications
  • When users ask about LangGraph, the LLM can invoke this tool
  • Results are incorporated into LLM responses

Use Cases

• Answering Questions: Providing accurate information about LangGraph concepts
• Troubleshooting: Helping users debug LangGraph implementations
• Code Examples: Retrieving relevant code snippets from documentation
• Best Practices: Sharing recommended approaches from official documentation

Connections

• Related Concepts: LangGraph, Model Context Protocol (MCP), MCP Tools
• Technical Components: Vector Store for Document Retrieval, Embedding Models
• Similar Tools: Documentation Query Tools, Retrieval-Augmented Generation
• Application Integration: MCP Host Applications, MCP Server Implementation

References

1. LangChain documentation on tool creation
2. LangGraph official documentation
3. Vector store implementation guides
4. MCP tool implementation examples

#LangGraph #MCP #Tools #DocumentRetrieval #VectorStore #Embeddings #RAG #AITools

Connections:

Sources:

• From: LangChain - Understanding MCP From Scratch