mcp-development-skill

name	mcp-development-skill
description	**Domain**: AI Infrastructure Use when this capability is needed.

MCP Development Skill

Domain: AI Infrastructure Inheritance: inheritable Version: 1.0.0 Last Updated: 2026-02-01

---

Overview

Complete guide to the Model Context Protocol (MCP)—an open standard for connecting AI assistants to external data sources and tools. Covers architecture, server development, client integration, and production deployment patterns.

---

What Is MCP?

The Problem MCP Solves

Before MCP:
┌─────────┐     ┌─────────┐     ┌─────────┐
│ Claude  │     │ ChatGPT │     │ Copilot │
└────┬────┘     └────┬────┘     └────┬────┘
     │               │               │
     ▼               ▼               ▼
┌─────────┐     ┌─────────┐     ┌─────────┐
│Custom   │     │Custom   │     │Custom   │
│Plugin A │     │Plugin A'│     │Plugin A"│
└─────────┘     └─────────┘     └─────────┘
  (3 different implementations for same data source)

After MCP:
┌─────────┐     ┌─────────┐     ┌─────────┐
│ Claude  │     │ ChatGPT │     │ Copilot │
└────┬────┘     └────┬────┘     └────┬────┘
     │               │               │
     └───────────────┼───────────────┘
                     ▼
              ┌────────────┐
              │ MCP Server │  (One implementation, many clients)
              └────────────┘

Core Concepts

Concept	Description
Host	AI application (Claude Desktop, VS Code, etc.)
Client	MCP client within the host, manages server connections
Server	Provides tools, resources, and prompts via MCP
Transport	Communication layer (stdio, HTTP+SSE)

MCP Architecture

┌─────────────────────────────────────────────────────────────┐
│                        MCP Host                             │
│  (Claude Desktop, VS Code, IDE, Custom App)                 │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌─────────────┐                                            │
│  │ MCP Client  │  Manages protocol, routing, lifecycle      │
│  └──────┬──────┘                                            │
│         │                                                   │
│    Transport Layer (stdio / HTTP+SSE)                       │
│         │                                                   │
└─────────┼───────────────────────────────────────────────────┘
          │
          ▼
┌─────────────────────────────────────────────────────────────┐
│                      MCP Server                             │
├─────────────────────────────────────────────────────────────┤
│  ┌───────────┐  ┌───────────┐  ┌───────────┐               │
│  │  Tools    │  │ Resources │  │  Prompts  │               │
│  │           │  │           │  │           │               │
│  │ Functions │  │ Data/Files│  │ Templates │               │
│  │ AI calls  │  │ AI reads  │  │ AI uses   │               │
│  └───────────┘  └───────────┘  └───────────┘               │
└─────────────────────────────────────────────────────────────┘

---

MCP Primitives

Tools

Functions the AI can execute:

// Tool definition
{
  name: "search_issues",
  description: "Search GitHub issues in a repository",
  inputSchema: {
    type: "object",
    properties: {
      repo: { type: "string", description: "owner/repo format" },
      query: { type: "string", description: "Search query" },
      state: {
        type: "string",
        enum: ["open", "closed", "all"],
        default: "open"
      }
    },
    required: ["repo", "query"]
  }
}

Tool Design Principles:

• Clear, action-oriented names
• Comprehensive descriptions (when to use, what it returns)
• Strict input schemas with validation
• Idempotent when possible
• Return structured results

Resources

Data the AI can read:

// Resource definition
{
  uri: "github://repo/owner/repo-name/issues",
  name: "Repository Issues",
  description: "All issues in the repository",
  mimeType: "application/json"
}

// Resource template (dynamic)
{
  uriTemplate: "github://repo/{owner}/{repo}/issues/{id}",
  name: "GitHub Issue",
  description: "A specific GitHub issue",
  mimeType: "application/json"
}

Resource Patterns:

• Static: Fixed URIs for known data
• Template: Dynamic URIs with parameters
• Subscription: Real-time updates (notifications)

Prompts

Reusable prompt templates:

{
  name: "code_review",
  description: "Generate a code review for changes",
  arguments: [
    {
      name: "diff",
      description: "The code diff to review",
      required: true
    },
    {
      name: "focus",
      description: "Areas to focus on (security, performance, style)",
      required: false
    }
  ]
}

---

Building MCP Servers

TypeScript Server (Recommended)

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new McpServer({
  name: "my-mcp-server",
  version: "1.0.0"
});

// Register a tool
server.tool(
  "get_weather",
  "Get current weather for a location",
  {
    location: {
      type: "string",
      description: "City name or coordinates"
    }
  },
  async ({ location }) => {
    const weather = await fetchWeather(location);
    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(weather, null, 2)
        }
      ]
    };
  }
);

// Register a resource
server.resource(
  "weather://current",
  "Current weather data",
  "application/json",
  async () => ({
    contents: [
      {
        uri: "weather://current",
        mimeType: "application/json",
        : .( ())
      }
    ]
  })
);


 transport =  ();
 server.(transport);

Python Server

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent

server = Server("my-mcp-server")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="get_weather",
            description="Get current weather for a location",
            inputSchema={
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["location"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_weather":
        weather = await fetch_weather(arguments["location"])
        return [TextContent(type="text", text=str(weather))]
    raise ValueError(f"Unknown tool: {name}")

async def main():
    async with stdio_server()  (read_stream, write_stream):
         server.run(read_stream, write_stream)

Server Project Structure

my-mcp-server/
├── package.json
├── tsconfig.json
├── src/
│   ├── index.ts          # Entry point
│   ├── server.ts         # Server setup
│   ├── tools/
│   │   ├── index.ts      # Tool registry
│   │   ├── search.ts     # Search tool
│   │   └── create.ts     # Create tool
│   ├── resources/
│   │   ├── index.ts      # Resource registry
│   │   └── data.ts       # Data resources
│   └── utils/
│       ├── auth.ts       # Authentication
│       └── cache.ts      # Caching
└── tests/
    └── tools.test.ts

---

Transport Protocols

stdio (Local)

Default for local servers:

// Claude Desktop config
{
  "mcpServers": {
    "my-server": {
      "command": "node",
      "args": ["/path/to/server/dist/index.js"],
      "env": {
        "API_KEY": "xxx"
      }
    }
  }
}

Characteristics:

• Process-based communication
• Secure (no network exposure)
• Simple deployment
• Best for local tools

HTTP + SSE (Remote)

For remote/shared servers:

┌────────────┐         HTTPS          ┌────────────┐
│   Client   │ ◄─────────────────────► │   Server   │
│            │    POST /message        │            │
│            │    GET  /sse (stream)   │            │
└────────────┘                         └────────────┘

Characteristics:

• Network-accessible
• Supports authentication
• Scalable (multiple clients)
• Requires security hardening

---

Client Integration

VS Code Integration

// Using MCP in VS Code extension
import { McpClient } from "@modelcontextprotocol/sdk/client/mcp.js";

const client = new McpClient({
  name: "vscode-client",
  version: "1.0.0"
});

// Connect to server
await client.connect(transport);

// List available tools
const { tools } = await client.listTools();

// Call a tool
const result = await client.callTool({
  name: "search_issues",
  arguments: { repo: "owner/repo", query: "bug" }
});

// Read a resource
const { contents } = await client.readResource({
  uri: "github://repo/owner/repo/readme"
});

Configuration Patterns

Per-User Config (Claude Desktop):

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

Workspace Config (VS Code):

// .vscode/mcp.json
{
  "servers": {
    "project-tools": {
      "command": "node",
      "args": ["./tools/mcp-server.js"]
    }
  }
}

---

Security Best Practices

Authentication

// API key from environment
const apiKey = process.env.SERVICE_API_KEY;
if (!apiKey) {
  throw new Error("SERVICE_API_KEY required");
}

// OAuth token refresh
async function getValidToken(): Promise<string> {
  if (tokenExpired()) {
    token = await refreshToken();
  }
  return token;
}

Input Validation

import { z } from "zod";

const SearchSchema = z.object({
  query: z.string().min(1).max(500),
  limit: z.number().int().min(1).max(100).default(10),
  filters: z.object({
    dateFrom: z.string().datetime().optional(),
    dateTo: z.string().datetime().optional()
  }).optional()
});

server.tool("search", "Search documents", SearchSchema, async (args) => {
  const validated = SearchSchema.parse(args);
  // Safe to use validated.query, validated.limit, etc.
});

Rate Limiting

import { RateLimiter } from "./utils/rate-limiter";

const limiter = new RateLimiter({
  maxRequests: 100,
  windowMs: 60 * 1000  // 1 minute
});

server.tool("expensive_operation", schema, async (args) => {
  if (!limiter.tryAcquire()) {
    return {
      content: [{
        type: "text",
        text: "Rate limit exceeded. Please try again later."
      }],
      isError: true
    };
  }
  // Proceed with operation
});

Sandboxing

// Restrict file system access
const ALLOWED_PATHS = ["/data", "/tmp"];

function validatePath(requestedPath: string): boolean {
  const resolved = path.resolve(requestedPath);
  return ALLOWED_PATHS.some(allowed =>
    resolved.startsWith(path.resolve(allowed))
  );
}

// Restrict network access
const ALLOWED_HOSTS = ["api.example.com", "data.example.com"];

function validateUrl(url: string): boolean {
  const parsed = new URL(url);
  return ALLOWED_HOSTS.includes(parsed.hostname);
}

---

Production Patterns

Error Handling

server.tool("risky_operation", schema, async (args) => {
  try {
    const result = await performOperation(args);
    return {
      content: [{ type: "text", text: JSON.stringify(result) }]
    };
  } catch (error) {
    // Log for debugging
    console.error("Operation failed:", error);

    // Return user-friendly error
    return {
      content: [{
        type: "text",
        text: `Operation failed: ${getUserFriendlyMessage(error)}`
      }],
      isError: true
    };
  }
});

function getUserFriendlyMessage(error: unknown): string {
  if (error instanceof AuthError) {
    return "Authentication failed. Please check your credentials.";
  }
  if (error instanceof RateLimitError) {
    return "Rate limit exceeded. Please try again in a few minutes.";
  }
  if (error instanceof ValidationError) {
     ;
  }
   ;
}

Logging & Observability

import { Logger } from "./utils/logger";

const logger = new Logger("mcp-server");

server.tool("search", schema, async (args, context) => {
  const requestId = context.requestId || crypto.randomUUID();

  logger.info("Tool called", {
    requestId,
    tool: "search",
    args: sanitizeForLogging(args)
  });

  const startTime = Date.now();
  try {
    const result = await performSearch(args);

    logger.info("Tool completed", {
      requestId,
      tool: "search",
      durationMs: Date.now() - startTime,
      resultCount: result.items.length
    });

    return { content: [{ type: "text", text: JSON.stringify(result) }] };
  } catch (error) {
    logger.error("Tool failed", {
      requestId,
      tool: "search",
      : .() - startTime,
      : error.
    });
     error;
  }
});

Caching

import { LRUCache } from "lru-cache";

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 5 * 60 * 1000  // 5 minutes
});

server.tool("fetch_data", schema, async ({ id }) => {
  const cacheKey = `data:${id}`;

  // Check cache
  const cached = cache.get(cacheKey);
  if (cached) {
    return { content: [{ type: "text", text: JSON.stringify(cached) }] };
  }

  // Fetch fresh
  const data = await fetchFromAPI(id);
  cache.set(cacheKey, data);

  return { content: [{ type: "text", text: JSON.stringify(data) }] };
});

---

Testing MCP Servers

Unit Testing Tools

import { describe, it, expect } from "vitest";
import { createTestServer } from "./test-utils";

describe("search tool", () => {
  it("returns results for valid query", async () => {
    const server = createTestServer();

    const result = await server.callTool({
      name: "search",
      arguments: { query: "test", limit: 5 }
    });

    expect(result.content).toHaveLength(1);
    expect(result.isError).toBeFalsy();

    const data = JSON.parse(result.content[0].text);
    expect(data.items).toHaveLength(5);
  });

  it("handles invalid input gracefully", async () => {
    const server = createTestServer();

    const result = await server.callTool({
      name: ,
      : { : , : - }
    });

    (result.).();
    (result.[].).();
  });
});

Integration Testing

import { spawn } from "child_process";
import { McpClient } from "@modelcontextprotocol/sdk/client/mcp.js";

describe("MCP Server Integration", () => {
  let serverProcess: ChildProcess;
  let client: McpClient;

  beforeAll(async () => {
    // Start server process
    serverProcess = spawn("node", ["dist/index.js"]);

    // Connect client
    client = new McpClient({ name: "test", version: "1.0.0" });
    await client.connect(new StdioClientTransport(serverProcess));
  });

  afterAll(() => {
    serverProcess.kill();
  });

  it("lists tools correctly", async () => {
    const { tools } = await client.listTools();
    expect(tools.map(t => t.name)).toContain("search");
  });
});

---

Common MCP Servers

Server	Purpose	Install
filesystem	Local file access	@modelcontextprotocol/server-filesystem
github	GitHub API	@modelcontextprotocol/server-github
postgres	Database queries	@modelcontextprotocol/server-postgres
puppeteer	Web scraping	@modelcontextprotocol/server-puppeteer
memory	Persistent memory	@modelcontextprotocol/server-memory

---

Activation Triggers

• "MCP", "Model Context Protocol"
• "MCP server", "MCP client"
• "tool server", "resource server"
• "Claude Desktop config", "mcp.json"
• "stdio transport", "SSE transport"
• "@modelcontextprotocol"

---

Quick Reference

MCP Server Checklist

• Define clear tool/resource purposes
• Implement comprehensive input validation
• Add proper error handling with user-friendly messages
• Set up logging for debugging
• Implement rate limiting for expensive operations
• Add caching where appropriate
• Write unit and integration tests
• Document configuration requirements
• Consider security (auth, sandboxing)

Tool vs Resource Decision

Use Tool When	Use Resource When
Action with side effects	Read-only data access
Requires input parameters	Static or template URI
Returns computed result	Returns stored content
May fail or have errors	Generally stable data

---

MCP Development skill — Building AI-accessible tools and data sources

---

Converted and distributed by TomeVault — claim your Tome and manage your conversions.