• An open, model‑agnostic protocol introduced by Anthropic in November 2024, designed to standardize how AI systems (huge language models, LLMs) connect with external data sources and tools via a JSON‑RPC interface.

• Often likened to a “USB‑C port for AI,” offering a universal interface rather than bespoke integrations per system.
  

Key Benefits of MCP

• Provides a standardized interface so LLMs can easily connect to multiple tools and data sources without custom adapters.

• Solves the “N×M” problem, removing the need to build a unique connector for every AI–tool combination.

• Ensures structured and validated exchanges, supporting better debugging, version control, and reliability in multi-agent systems.

• Gives developers access to a growing set of pre-built servers (e.g., GitHub, Slack, Google Drive) that can be reused across AI apps.

• Enables secure, offline-capable MCP servers, allowing agents to run locally without exposing sensitive data.



• Supports OAuth 2.1 and authorization safeguards, ensuring secure communication and controlled resource access.

• Allows LLMs to autonomously orchestrate multi-step tasks, deciding which tools to use and chaining actions together.

Examples

• Pre-built servers: GitHub, Google Drive, Slack, PostgreSQL, Puppeteer.

• Enterprise adoption: Block (Square), Replit, Codeium, Sourcegraph.

• Platform support:

  • OpenAI → ChatGPT desktop app, Agents SDK, Responses API.

  • Google DeepMind → Gemini models.

  • Microsoft → Windows AI Foundry, Copilot Studio.

• Claude Desktop → Can browse local files via MCP.

Use Cases

• Replit/Zed assistants read project context.

• Pull CRM, docs, and knowledge bases into workflows.

• Natural-language queries → SQL → results.

• Search docs → query DB → send Slack message.

• LLMs plan & execute resource functions.

References

• Model Context Protocol – Wikipedia

• Introduction – Model Context Protocol