Mcp Server Creation

# MCP Server Creation

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

MCP Server Creation is the skill of building Model Context Protocol servers that expose tools, resources, and capabilities to AI agents through a standardized interface. MCP has emerged as the universal integration layer for AI agent tooling — a single protocol that lets any compliant agent (Claude Code, Cursor, Codex, and others) discover and invoke server-side tools without custom integration code. Building production-quality MCP servers on Cloudflare Workers enables global, low-latency tool access with zero infrastructure management.

This skill draws from the production MCP server powering the Buddy™ agent at [googleadsagent.ai™](https://googleadsagent.ai), deployed on Cloudflare Workers in the `workers/buddy-agent/` directory. That server exposes Google Ads analysis tools, knowledge base search, entity memory management, and web search capabilities through MCP, making them available to any MCP-compatible client. The architecture handles authentication, rate limiting, and multi-tenant isolation at the edge.

The MCP specification defines three core primitives: tools (executable functions the agent can invoke), resources (read-only data the agent can access), and prompts (reusable prompt templates). A well-designed MCP server exposes a focused set of high-value tools with clear schemas, meaningful descriptions, and robust error handling. The transport layer supports both stdio (for local development) and SSE/HTTP (for remote deployment), with authentication integrated at the transport level.

## Use When

- You need to expose custom tools to AI agents through a standardized protocol
- Building integrations that work across Claude Code, Cursor, and other MCP clients
- Deploying agent tools to Cloudflare Workers for global edge access
- Creating a tool marketplace or shared tooling infrastructure for your organization
- Migrating from proprietary tool interfaces to the open MCP standard
- Your agent needs tools that require server-side secrets or API access

## How It Works

```mermaid
graph TD
    A[AI Agent / MCP Client] -->|MCP Protocol| B[Transport Layer]
    B -->|stdio| C[Local Development]
    B -->|SSE/HTTP| D[Remote Server]
    D --> E[Cloudflare Worker]
    E --> F[Authentication]
    F --> G[Tool Router]
    G --> H[Tool: search_knowledge]
    G --> I[Tool: analyze_campaign]
    G --> J[Tool: get_metrics]
    H --> K[Knowledge Base]
    I --> L[Google Ads API]
    J --> L
    G --> M[Resource: config]
    G --> N[Resource: schema]
```

The MCP server sits between the AI agent client and the actual data sources or APIs. The transport layer handles protocol negotiation (stdio for local, SSE/HTTP for remote). For Cloudflare Workers deployment, incoming requests are authenticated (OAuth, API key, or JWT), then routed to the appropriate tool handler. Each tool handler implements the business logic — calling external APIs, querying databases, or performing computations — and returns structured results that the agent can reason over.

## Implementation

**MCP Server on Cloudflare Workers:**

```typescript
import { McpAgent } from "agents/mcp";
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

export class BuddyMCPServer extends McpAgent {
  server = new McpServer({
    name: "buddy-agent",
    version: "1.0.0",
  });

  async init() {
    this.server.tool(
      "search_knowledge",
      "Search the Google Ads knowledge base for patterns and best practices matching a query. Use BEFORE making recommendations.",
      { query: z.string(), category: z.enum(["bidding", "targeting", "creative", "b