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As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data.
The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is.
Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server.
This is why 𝐌𝐂𝐏 is highly relevant for production AI systems:
• 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source.
• 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client.
• 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities.
𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven.
Demo link in the first comment.
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