AI in practice, part 5: MCP, the Model Context Protocol

Mon, 26 Oct 2026 18:40:00 -0400

Before USB, every device needed its own port. Before MCP, every AI application needed its own connector for every data source. The Model Context Protocol is the common plug.

The problem it solves
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Part 4 showed how a model uses tools. The catch: each AI application (a chat app, an IDE assistant, an agent framework) described tools its own way. Connecting M applications to N data sources meant up to M × N custom integrations.

AI in practice, part 4: From RAG to tools and agents

Mon, 26 Oct 2026 18:30:00 -0400

RAG lets a model read what it didn’t know. Tools let it do things. An agent is what you get when the model decides, step by step, which tools to use until the job is done.

Three levels of “the model knows something”
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Only its training. The model answers from patterns learned before its cutoff. It can’t see your files and doesn’t know today’s date unless you tell it.
Retrieval (RAG). Your application searches your documents and pastes the relevant passages into the prompt. The model reads, then answers. I built one of these here. The application decides what to retrieve.
Tools. The model itself asks for something to be done: “search the docs for X”, “run this query”, “create the ticket”. The model decides when and how; your application carries it out.

Function calling, step by step
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With tool use (often called function calling), you describe each tool to the model: a name, a description and a JSON schema for its arguments.

Tools on Samarthya

AI in practice, part 5: MCP, the Model Context Protocol

The problem it solves #

AI in practice, part 4: From RAG to tools and agents

Three levels of “the model knows something” #

Function calling, step by step #

The problem it solves
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Three levels of “the model knows something”
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Function calling, step by step
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