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Components that expose tools, resources, and prompts to host applications

Core Idea: MCP Servers are software components that package and expose standardized tools, data resources, and prompt templates to Large Language Models through the Model Context Protocol, enabling LLMs to interact with external systems.

Key Elements

Architecture

Types of MCP Servers

Implementation Options

Common Server Capabilities

Tool Examples

Resource Examples

Prompt Examples

Implementation Steps

  1. Define Capabilities: Identify tools, resources, and prompts to expose
  2. Select SDK: Choose appropriate language SDK (Python, JavaScript)
  3. Implement Handlers: Create handler functions for each capability
  4. Configure Server: Set up JSON-RPC endpoints and message handling
  5. Test Integration: Verify functionality with host applications
  6. Document Usage: Create clear documentation for users

Code Example (Python)

from mcp import MCPServer

# Create server instance
server = MCPServer()

# Define and register a tool
@server.tool(
    name="weather.current",
    description="Get current weather for a location",
    parameters={
        "location": {
            "type": "string",
            "description": "City name or coordinates"
        }
    }
)
async def get_weather(location: str):
    # Implementation logic here
    return {"temperature": 72, "conditions": "sunny"}

# Start the server
server.start()

Connections

References

  1. Anthropic MCP GitHub Repository: github.com/anthropics/anthropic-cookbook
  2. MCP Server Documentation: modelcontextprotocol.io/server-dev
  3. Server Example Implementations: github.com/anthropics/mcp-examples

#MCP #MCPServer #ToolIntegration #AITools #ServerImplementation #JSON-RPC #LLMTools

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