MCP Guide: Connecting AI Coding Agents with External Tools

When you start delegating code to AI through vibe coding, you quickly hit a wall with requests like "fetch some data from the DB." While AI is great at writing code, accessing external services directly requires a separate connection. MCP solves exactly this problem. It's a standard protocol that enables AI coding agents to communicate directly with databases, web crawlers, messaging platforms, design tools, and more.

With MCP, the possibilities of vibe coding expand dramatically. Beyond simply generating code, AI can query real data, crawl the web, and call external APIs.

What is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is an open standard protocol that connects AI models with external tools. Think of it as "USB-C for the AI world." Just as USB-C connects any device through a single port, MCP allows any AI tool to connect to external services through a single protocol.

Anthropic first released it in November 2024, and OpenAI announced its adoption in March 2025. Google and Microsoft soon followed, making it a de facto industry standard. In December 2025, it was donated to the Linux Foundation's AAIF (AI Alliance for Interoperability Foundation), establishing it as a truly open standard not tied to any single company.

MCP Ecosystem Overview

The MCP ecosystem is growing rapidly. The numbers speak for themselves.

• Monthly SDK downloads: Over 97 million
• Active MCP servers: Over 10,000
• MCP Registry listings: Over 2,000

Major MCP clients include Claude Desktop, ChatGPT Desktop, Cursor, VS Code (GitHub Copilot), and Gemini CLI. Virtually all major AI coding tools support MCP, so once you set it up, you can use the same MCP servers regardless of which tool you choose.

Popular MCP Servers

Here's a summary of frequently used MCP servers in vibe coding. Choose the ones that match your project's needs.

Setting Up MCP

Adding an MCP server in Claude Code is simple. It takes just one command: claude mcp add.

Basic syntax:

claude mcp add <server-name> <command> [args...]

Example - Adding Firecrawl:

claude mcp add firecrawl-mcp -- npx -y firecrawl-mcp

Example - Adding Slack:

claude mcp add slack -- npx -y @anthropic/mcp-server-slack

Settings are managed across 3 scopes. --scope user is a global setting for all projects, --scope local is for the current project only, and --scope project creates a .mcp.json file that can be shared with team members. For team projects, committing .mcp.json to Git ensures everyone uses the same MCP environment.

OpenAI also officially adopted MCP in March 2025. You can connect MCP servers in the ChatGPT Desktop app, and Codex CLI supports MCP as well. The setup process is similar to Claude Code—add a server and use its tools. Since MCP is an industry standard, any MCP server you configure works the same way whether you're using Claude Code or Codex.

MCP Use Cases in Vibe Coding

Connecting MCP servers dramatically expands what you can do with vibe coding. Here are some representative use cases.

DB Integration: With Supabase or PostgreSQL MCP, AI automatically understands your DB schema and generates appropriate queries. Natural language requests like "show me the 10 most recent signups from the users table" execute directly.

Web Crawling: Firecrawl MCP lets AI collect data directly from the web. It can gather competitor pricing to create comparison tables or reference technical documentation while writing code.

Browser Testing: With Playwright MCP, AI can automatically open web pages it built in a browser for testing. It captures screenshots to verify layouts and immediately fixes any bugs it discovers.

Design Integration: Figma MCP lets you read component information directly from design files and convert them to code.

MCP vs CLI

You don't need to connect MCP for every tool. Tools like GitHub, PostgreSQL, and npm already have powerful CLIs. AI coding agents can call these CLIs directly through Bash, so adding MCP for them would just waste context. Commands like git status, psql, and npm install work perfectly fine without MCP.

However, tools like Figma, Notion, Slack, and Blender are a different story—they don't have CLIs. Reading component information from design files, searching Notion documents, or sending messages to Slack channels is impossible via CLI. These tools are only accessible through APIs, and MCP is the sole bridge that opens those APIs to AI.

The bottom line: MCP's real value lies in connecting AI to areas that CLI can't reach. There's no need to use MCP for tools that already have CLIs—just choose the right approach for your project.

MCP Precautions

MCP is powerful, but connecting too many servers can backfire. Here are some things to watch out for.

Context Consumption: The more MCP servers you connect, the more of AI's context window they consume. With 10+ servers connected, MCP tool descriptions alone can take up over 40% of the total context, leaving less room for actual coding.

Security Risks: MCP servers grant access to external services. Connecting unauthenticated servers can expose sensitive data. Only use official or verified servers, and be careful with API key management.

Recommendations: Selectively connect only the servers your project actually needs. Generally, 3-5 servers is appropriate. You can remove unused servers with the claude mcp remove command.

Conclusion

MCP is the key infrastructure that evolves vibe coding from simple "code autocomplete" to "autonomous agents." AI doesn't just write code—it can query databases, crawl the web, and send notifications through messaging platforms. As an industry standard adopted by Anthropic, OpenAI, Google, and Microsoft, learning MCP means you can leverage it with any AI tool.

If you haven't tried MCP yet, we recommend starting by connecting just one server you need most. Starting with versatile servers like Firecrawl or Notion will let you immediately experience the value MCP brings.