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Executable functions exposed to LLMs through the Model Context Protocol

Core Idea: MCP Tools are executable functions that can be discovered and called by LLM applications through the Model Context Protocol, allowing AI models to interact with external systems, retrieve information, and perform actions that extend beyond their built-in capabilities.

Key Elements

Tool Structure

• Registration: Tools are defined as functions and registered with the MCP server
• Naming: Each tool has a unique identifier for discovery and invocation
• Arguments: Tools accept structured input parameters, typically as JSON
• Return Values: Tools return results that can be incorporated into the LLM's context

Implementation Patterns

• Function Decorators:

  @server.tool("search_documents")
  def search_documents(query: str) -> str:
      # Implementation that searches documents
      return results

• Tool Adaptation:

# Adapting existing LangChain tools for MCP
from langchain.tools import Tool

langchain_tool = Tool(
    name="calculator",
    func=lambda x: eval(x),
    description="Useful for performing calculations"
)

# Register with MCP server
server.add_tool("calculator", langchain_tool.func)

Common Tool Categories

• Information Retrieval: Search tools, vector store query tools, knowledge base lookup
• Computation: Calculators, data processors, code execution environments
• External API Access: Weather services, stock information, web search
• System Integration: File system access, database queries, service controls
• Processing Tools: Document summarization, analysis, or transformation

Tool Discovery and Execution

1. Discovery Phase:

   • Host applications query the server for available tools
   • Servers provide tool metadata including names, descriptions, and parameter schemas

2. Execution Flow:

   • LLM decides to use a tool based on user request
   • Host application formats tool call as a JSON-RPC request
   • Server executes the tool and returns results
   • Host incorporates results back into the LLM's context

Development Best Practices

• Atomic Functionality: Each tool should perform a single, well-defined function
• Error Handling: Tools should handle errors gracefully and return informative messages
• Performance Optimization: Tools should be efficient to avoid latency in LLM interactions
• Statelessness: Tools should be designed to be stateless where possible
• Documentation: Clear descriptions help LLMs understand when and how to use tools

Connections

• Related Concepts: Model Context Protocol (MCP), MCP Architecture, MCP Server
• Complementary Capabilities: MCP Resources, Function Calling in LLMs
• Implementation Examples: LangGraph Query Tool, Vector Store Integration
• Broader Concepts: Tool-using LLMs, Agentic AI, ReAct Pattern

References

1. Model Context Protocol Specification: modelcontextprotocol.io
2. Lan (LangChain) tutorial on MCP implementation
3. Examples of tool integration in Cursor, Windsurf, and Claude Desktop
4. Best practices for developing MCP tools from the MCP community

#MCP #Tools #FunctionCalling #AIAgents #ExtendedCapabilities #ToolUsingLLMs #SystemIntegration

Connections:

Sources:

• From: LangChain - Understanding MCP From Scratch