Building AI tools with the Model Context Protocol (MCP): a developer guide

Model Context Protocol is the USB‑C of AI tools: a single, open way for assistants to talk to databases, APIs, and apps instead of bespoke, one‑off integrations. It standardises how assistants discover, describe, and call tools (plus access resources and prompts), so the same MCP server can be reused across Claude, ChatGPT, Cursor, Windsurf, and others. For anyone building agents, that means less glue code, easier interoperability, and an emerging ecosystem of “plug‑and‑play” tools.
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This tutorial walks through:

What MCP is and why it matters

How an MCP server is structured

Wiring it into AI assistants

Tool discovery, versioning, and security

A real “from scratch” integration: a simple but genuinely useful server

What MCP is and why it matters

MCP is an open standard for connecting AI assistants to external systems over a consistent client-server protocol. An MCP client (e.g. an assistant app) connects to one or more MCP servers, each of which exposes tools (functions), resources (data), and prompts in a machine‑readable way.
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Without MCP, each assistant has a custom tool format and plugin framework; with MCP, services expose a single MCP server that any compatible client can talk to. This is what makes “MCP app stores” and reusable tool libraries realistic: instead of re‑writing the same integration five ways, you implement MCP once.
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Conceptual architecture

At a high level, MCP defines:

A JSON‑RPC-style protocol with methods like tools/list, tools/call, resources/list, etc.
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Schemas for how tools and resources are described, including parameter types.
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A transport layer (typically stdio, WebSocket, or other streams) between client and server.
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The LLM never calls your API directly; instead, the assistant

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Rizwan Saleem | https://rizwansaleem.co