๐ช๐ต๐ ๐๐๐ฒ๐ฟ๐ ๐๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐๐ฎ๐ฟ๐ฒ ๐๐ฏ๐ผ๐๐ ๐ ๐๐ฃ
If youโve built LLM-powered assistants, you know the pain:
โข Manual wrappers for every tool
โข Prompt spaghetti for each workflow
โข Rewriting integrations for every new app
Scaling AI is messy because models arenโt designed to know how to use toolsโand every integration becomes brittle.
Thatโs where MCP (Model Context Protocol) comes in:
๐ฆ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐ฐ๐ผ๐ป๐ฐ๐ฒ๐ฟ๐ป๐ โ Hosts orchestrate, clients handle communication, servers expose tools and resources
๐ฅ๐ฒ๐๐๐ฎ๐ฏ๐น๐ฒ ๐ฐ๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ถ๐ฒ๐ โ Write a tool once, use it across multiple assistants
๐ฆ๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ ๐บ๐ฒ๐๐๐ฎ๐ด๐ถ๐ป๐ด โ Typed JSON-RPC ensures predictable, debuggable interactions
๐ฆ๐ฐ๐ฎ๐น๐ฎ๐ฏ๐น๐ฒ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ โ No more MรN integration chaos
Think of it as the infrastructure layer LLMs were always missing.
Example: A Research Assistant that reads files, queries APIs, searches the web. With MCP, each capability is a server. Multiple assistants can reuse the same servers โ no glue code, no duplication, just modular AI.If we write AI agent without MCP we need to write same tools again again.More time more money
The takeaway: MCP turns brittle AI hacks into composable, scalable systems.
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