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Cover image for ๐—ช๐—ต๐˜† ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐—œ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐—–๐—ฎ๐—ฟ๐—ฒ ๐—”๐—ฏ๐—ผ๐˜‚๐˜ ๐— ๐—–๐—ฃ
Faisal Mahamud
Faisal Mahamud

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๐—ช๐—ต๐˜† ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐—œ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐—–๐—ฎ๐—ฟ๐—ฒ ๐—”๐—ฏ๐—ผ๐˜‚๐˜ ๐— ๐—–๐—ฃ

๐—ช๐—ต๐˜† ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐—œ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐—–๐—ฎ๐—ฟ๐—ฒ ๐—”๐—ฏ๐—ผ๐˜‚๐˜ ๐— ๐—–๐—ฃ
If youโ€™ve built LLM-powered assistants, you know the pain:
โ€ข Manual wrappers for every tool
โ€ข Prompt spaghetti for each workflow
โ€ข Rewriting integrations for every new app

Scaling AI is messy because models arenโ€™t designed to know how to use toolsโ€”and every integration becomes brittle.

Thatโ€™s where MCP (Model Context Protocol) comes in:

๐—ฆ๐—ฒ๐—ฝ๐—ฎ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฟ๐—ป๐˜€ โ€“ Hosts orchestrate, clients handle communication, servers expose tools and resources
๐—ฅ๐—ฒ๐˜‚๐˜€๐—ฎ๐—ฏ๐—น๐—ฒ ๐—ฐ๐—ฎ๐—ฝ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐—ถ๐—ฒ๐˜€ โ€“ Write a tool once, use it across multiple assistants
๐—ฆ๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐—ฑ ๐—บ๐—ฒ๐˜€๐˜€๐—ฎ๐—ด๐—ถ๐—ป๐—ด โ€“ Typed JSON-RPC ensures predictable, debuggable interactions
๐—ฆ๐—ฐ๐—ฎ๐—น๐—ฎ๐—ฏ๐—น๐—ฒ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ โ€“ No more Mร—N 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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