Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open, client-server communication standard that lets AI applications connect to external tools, resources, and predefined prompts through a consistent interface.

MCP defines how a client discovers and invokes server-provided capabilities:

  • Tools are callable actions or API requests that the client can ask the server to perform.
  • Resources are structured data or context, such as files, database tables, documents, and schemas, accessible by URI, which the client or model can reference.
  • Prompts are predefined prompt templates that clients can fetch, fill with arguments, and execute to drive structured interactions with language models.

MCP also lets a server ask for more information mid-call. Through elicitation, a server can answer a request by asking the client to gather extra input or a confirmation from the user, and the client then retries the original request with the user’s answers attached.

As of protocol version 2026-07-28, MCP is a stateless protocol: every request carries its own protocol version and capabilities, and clients that want to learn a server’s capabilities up front can call the optional server/discover request.

MCP encodes messages using JSON-RPC 2.0 over one of its two standard transports: stdio for local processes or streamable HTTP for remote servers.

The goal is interoperability and portability across assistants, hosts, and integrations, so developers can build a connector once and reuse it in many AI apps.

Python MCP: Connect Your LLM With the World

Tutorial

Python MCP Server: Connect LLMs to Your Data

Learn how to build a Model Context Protocol (MCP) server in Python. Connect tools, prompts, and data to AI agents like Cursor for smarter assistants.

intermediate ai

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By Leodanis Pozo Ramos • Updated Oct. 5, 2026
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