Model Context Protocol (MCP): What It Is, How It Works, and How to Use Context7

Introduction

Model Context Protocol (MCP) is an open standard launched by Anthropic in late November 2024 that standardizes how large language models (LLMs) like Claude or ChatGPT can access external tools, data, APIs, files, and services.

Think of it as the USB‑C port for AI: a universal connector that lets any AI plug into any compatible tool or data source without custom coding.

In March–April 2025, major AI platforms such as OpenAI (ChatGPT) and Google DeepMind (Gemini) officially adopted MCP—meaning any compliant LLM can now connect to MCP-compatible services seamlessly.

By May 2025, directories listed thousands of available MCP servers providing prebuilt integrations—from Slack and GitHub to Figma and Postgres.

How the Model Context Protocol Works

1. Client‑Server Architecture

MCP clients (LLM hosts like Claude Desktop, Cursor, Windsurf, ChatGPT Agents) send structured JSON‑RPC calls.

MCP servers expose tools, APIs, documents, code libraries, or UI controls for the AI to query.

2. Tool Integration

Clients can invoke actions like “search this CRM, export to Slack” in one natural‑language request.

Behind the scenes, the AI issues MCP tool calls, and servers return structured results for chaining actions across services.

3. Standardization & Interoperability

Developers can build/customize MCP servers in Python, JavaScript, TypeScript, C#, Java (all supported via SDKs).

Once MCP‑compatible, any client can use them—no need for per‑integration coding.

4. Security & Control

Servers handle authentication and authorization.

Clients only get access to what the server exposes.

Known risks include prompt injection, tool‑poisoning, and cross‑tool data exfiltration—actively being audited and mitigated.

Popular MCP Servers (Including Context7)

Among the most widely used MCP servers:

Context7 — Provides live, version‑specific code documentation and examples to AI clients, reducing hallucinations in coding tasks.

Playwright MCP — Automates UI and test code features with browser tools.

Needle MCP — Access to internal documentation & knowledge bases.

Supabase / PostgreSQL MCPs — Direct database queries.

Notion MCP — Manage Notion pages and databases.

Figma MCP — Access and edit Figma projects.

Zapier MCP — Automate workflows with hundreds of apps.

Web Search MCP — Real-time search capabilities.

Quick Tutorial: Using Context7 MCP

Prerequisites

Node.js v18+

An MCP‑compatible client (Claude Desktop, Cursor, VS Code with MCP plugin)

Installation Example (Cursor)

Edit your mcp.json:

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

Installation Example (Claude Desktop with Smithery CLI)

npx -y @smithery/cli install @upstash/context7-mcp --client claude

Using Context7

In your AI prompt, type:

use context7

Ask for a context‑aware instruction, e.g.:

Generate example Next.js code using version 14.3.0

The AI client calls the Context7 MCP server, which fetches real‑time docs and returns structured results.

The AI uses that data to generate accurate, up‑to‑date code.

Result:

✅ Real‑time documentation

✅ Reduced hallucinations

✅ Version‑accurate code

Security Notes

Only connect to trusted MCP servers.

Review the permissions each server requests.

Keep clients and servers updated to patch security vulnerabilities.

Conclusion

The Model Context Protocol is rapidly becoming the standard for connecting AI models to the real world.

With servers like Context7, you can give your AI real‑time, accurate data and powerful capabilities without manual integration.

As MCP adoption grows, expect more ready‑to‑use integrations—making AI workflows faster, smarter, and more reliable.