mcp-development

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Model Context Protocol (MCP) server development and AI/ML integration patterns. Covers MCP server implementation, tool design, resource handling, and LLM integration best practices. Use when developing MCP servers, creating AI tools, integrating with LLMs, or when asking about MCP protocol, prompt engineering, or AI system architecture.

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npx skill4agent add kiraneswaran/engineering-skills mcp-development

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mcp-server-developmentllm-integrationai-tool-designresource-handlingprompt-engineering

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MCP Development

What is MCP?

The Model Context Protocol (MCP) is an open protocol that enables AI assistants to interact with external tools, data sources, and services in a standardized way.

Core Concepts

ConceptDescription
ToolsFunctions the AI can call
ResourcesData the AI can read
PromptsPre-defined prompt templates
TransportsCommunication methods (stdio, HTTP)

MCP Server Structure

typescript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({
  name: "my-mcp-server",
  version: "1.0.0",
}, {
  capabilities: {
    tools: {},
    resources: {},
  }
});

// Define tools
server.setRequestHandler("tools/list", async () => ({
  tools: [
    {
      name: "get_data",
      description: "Fetch data from the API",
      inputSchema: {
        type: "object",
        properties: {
          id: { type: "string", description: "Item ID" }
        },
        required: ["id"]
      }
    }
  ]
}));

// Handle tool calls
server.setRequestHandler("tools/call", async (request) => {
  const { name, arguments: args } = request.params;
  
  if (name === "get_data") {
    const result = await fetchData(args.id);
    return { content: [{ type: "text", text: JSON.stringify(result) }] };
  }
  
  throw new Error(`Unknown tool: ${name}`);
});

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Tool Design Best Practices

Clear Descriptions

typescript
{
  name: "search_documents",
  description: "Search documents by keyword. Returns matching documents with relevance scores. Use when the user asks to find or search for specific content.",
  inputSchema: {
    type: "object",
    properties: {
      query: {
        type: "string",
        description: "Search query - can include multiple keywords"
      },
      limit: {
        type: "integer",
        description: "Maximum number of results (default: 10)",
        default: 10
      }
    },
    required: ["query"]
  }
}

Error Handling

typescript
server.setRequestHandler("tools/call", async (request) => {
  try {
    const result = await executeTool(request.params);
    return { content: [{ type: "text", text: result }] };
  } catch (error) {
    return {
      content: [{
        type: "text",
        text: `Error: ${error.message}`
      }],
      isError: true
    };
  }
});

Resources

typescript
server.setRequestHandler("resources/list", async () => ({
  resources: [
    {
      uri: "file:///docs/readme.md",
      name: "README",
      description: "Project documentation",
      mimeType: "text/markdown"
    }
  ]
}));

server.setRequestHandler("resources/read", async (request) => {
  const { uri } = request.params;
  const content = await readResource(uri);
  return {
    contents: [{
      uri,
      mimeType: "text/markdown",
      text: content
    }]
  };
});

Transport Options

TransportUse Case
stdioLocal CLI tools
HTTP/SSEWeb services, remote servers

Security Considerations

  • Validate all input parameters
  • Sanitize file paths
  • Rate limit API calls
  • Don't expose secrets
  • Log all tool invocations
  • Handle timeouts gracefully

Detailed References

  • MCP Patterns: See references/mcp-patterns.md
  • AI/ML Integration: See references/ai-ml-integration.md