Model Context Protocol (MCP) is an open protocol that defines how AI models (agents) can communicate with a central server in a structured and reusable way.
- Think of it like a plug-and-play system:
- You build a model or an agent.
- Register it with an MCP server.
- Any application can now ask that agent to perform a task (like summarizing text, fetching news, etc.) using a standard JSON-based API.
But why MCP ?
Most AI projects are such that — input goes into one model and gives one output.
- MCP changes the game by allowing you to:
- Build modular, reusable AI agents.
- Chain them together.
- Swap one model with another without changing the entire app.
Lets say :
- You want to fetch the latest headlines from NewsAPI
- Summarize them using ChatGPT or OpenRouter. (However ,both are paid 🥲)
- Serve the summary through an MCP server
MCP makes AI projects modular, scalable, and clean. 😉
I'm currently working on a project based on the Model Context Protocol (MCP). If anyone has additional resources or insights, I'd really appreciate it if you could share them—it would be a great help!
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