If you've ever tried building an AI agent system in production, you know the pain. During the construction of the
What Exactly is the Model Context Protocol?
Initially proposed by Anthropic and rapidly adopted by a coalition of open-source companies, MCP is an open standard for connecting AI models with data sources and tools.
The design premise is elegantly simple, separating the architecture into two independent pieces:
MCP Hosts: Applications or frameworks where the LLM resides (for example, the Claude desktop app, a LangChain script, or your own Python application).
MCP Servers: Lightweight, small programs that expose data or tools to the Host following a strict standard contract (for example, an MCP server that reads your PostgreSQL database, another that reads your GitHub repository).
The magic happens in the middle. The Host (the LLM) tells the MCP Server: "What tools and resources do you have available?". The server responds in a universal format. From there, the LLM can read, write, or execute actions without the developer having had to write a proprietary integration between that specific model and that specific tool.
Comparison: MCP vs Native Tool Calling
On this blog, I have vehemently defended why or any system where you need to connect AI agents with legacy ERPs, PLMs, or document repositories, my recommendation is to bet on isolating your connectors. Today it might be through independent Python functions, and tomorrow, probably, wrapping those same functions in an MCP Server.
Just as USB killed hundreds of proprietary connectors, MCP has the potential to finally democratize LLM access to the "muscle" of enterprise data.
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