ReviseAlgo Logo

Building AI Agents

MCP (Model Context Protocol)

The USB-C standard for connecting AI agents to tools, data, and APIs.

Interview: High - Emerging industry standard by Anthropic, adopted by OpenAI, Google, Microsoft.

The Tool Integration Problem

Before MCP, every AI application had to build custom integrations for each tool. If you had 10 tools and 5 AI platforms, you built 50 connectors. Model Context Protocol (MCP), created by Anthropic, standardizes this into a universal protocol — like USB-C for AI.

MCP Architecture

Key Concepts

  • MCP Host: The AI application (Claude Desktop, VS Code, your app) that needs to access tools.
  • MCP Client: Lives inside the host. Maintains a 1:1 connection with each MCP server.
  • MCP Server: Lightweight service that exposes tools, resources (data), and prompts via the MCP protocol.
  • Transport: Servers connect via stdio (local) or SSE/HTTP (remote).

What MCP Servers Expose

  • Tools: Functions the LLM can call (e.g., query_database, create_issue)
  • Resources: Read-only data the LLM can access (e.g., file contents, DB schemas)
  • Prompts: Reusable prompt templates that the host can surface to users

Use Cases

Connecting Claude Desktop to your local filesystem, databases, and Git repos

Building IDE copilots that can read project files, run tests, and create PRs

Creating enterprise tools that connect to Slack, Jira, and internal APIs through a single protocol

Publishing reusable MCP servers that any AI platform can consume

Common Mistakes

Exposing write operations without authentication — MCP servers should enforce access control

Building monolithic MCP servers — keep servers focused on one domain (DB, files, API)

Not using the resources primitive for static context — stuffing everything into tools wastes tokens

Ignoring transport security — remote SSE servers need HTTPS and auth tokens