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How to Build an AI Agent with Model Context Protocol (MCP) — Step by Step

A practical guide to building production-grade AI agents with MCP: server design, tool schemas, memory, and hardening for real users.

T
Tidqom Editorial
July 19, 2026 · 9 min read
How to Build an AI Agent with Model Context Protocol (MCP) — Step by Step — AI

How to Build an AI Agent with Model Context Protocol (MCP)

Sun Jul 19 2026 · tidqom.com engineering

MCP has quietly become the default way agents talk to tools in 2026.

What is MCP

Related: Midjourney vs DALL-E vs Flux: A Practical Image-Tool Comparison Framework →

Related: Anthropic Expands Claude Cowork to Mobile and Web, Broadening the Agent Surface →

A small JSON-RPC protocol that lets any LLM discover and call tools exposed by any server. Think USB-C for AI agents.

Step 1: Design tools

Related: 9 Free AI Coding Tools Every Developer Should Try in 2026 →

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Related: Claude 4.5 vs GPT-5.5: Which AI Coding Model Wins in 2026? →

  • Clear names (create_invoice, not do_stuff).
  • Narrow JSON schemas.
  • Compact, serializable results.
  • Descriptions the model reads at planning time.

Step 2: Build the server

Related: Sora 2 Review: OpenAI's Video Model Is Finally Useful for Real Work →

Related: الذكاء الاصطناعي الوكيلي (Agentic AI): الدليل الشامل 2026 →

ts
import { McpServer } from "@modelcontextprotocol/sdk/server";
import { z } from "zod";

const server = new McpServer({ name: "invoicing", version: "1.0.0" }); server.tool("create_invoice", { customerId: z.string(), amountCents: z.number().int().positive() }, async ({ customerId, amountCents }) => { const inv = await db.invoices.create({ customerId, amountCents }); return { content: [{ type: "text", text: JSON.stringify(inv) }] }; }); server.listen();

terminal

Step 3: Wire the model

Related: How to Clone Your Voice with AI in 2026 (Free and Paid Options) →

Related: Anthropic Extends Free Claude Fable 5 Access Through July 19 — Again →

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Loop: model plans → calls tool → server executes → model incorporates → repeat until stepCountIs(50).

Step 4: Memory

Related: Anthropic Localizes Claude Pricing for India, Its Biggest Market After the US →

  • Short-term buffer with summarization at 20k tokens.
  • Long-term vector store retrieved via search_memory tool.

Step 5: Harden

Related: Anthropic's Claude Moves Into Healthcare Claims and Care Management →

  • Auth on every tool.
  • Rate limits per user.
  • Hard cost caps.
  • Approval gates for spend/send.
  • Idempotency keys.

Step 6: Evaluate

30–100 real tasks. Score on correctness, efficiency, safety. Regression-test on every change.

Pitfalls

  1. Descriptions too long.
  2. Overlapping tools.
  3. No approval gates.
  4. Model-side authorization.
  5. No cost caps.

FAQ

MCP vs function calling? MCP standardizes so any client can use any server. Which SDK? TypeScript for edge/serverless; Python for ML stacks. Open-source models? Llama 4, DeepSeek R2, Qwen 3 all support tool calling. Secure? Transport only — auth, limits, approvals, audits are your job.

Related

Keywords

model context protocol tutorial, build ai agent mcp, mcp server example, ai agent architecture, llm tool calling, claude mcp tools, openai agents sdk, ai agent memory, ai agent framework 2026, mcp vs function calling.

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