Model Context Protocol
The Model Context Protocol (MCP) is a standard developed by Anthropic that enables AI models to use tools by defining a structured format for tool descriptions, calls, and responses.
It defines a standardized way for LLMs to discover, call, and use tools, often hosted on local or remote servers, via HTTP + JSON RPC2.0.
Function calling is like giving LLM a calculator app.
MCP is like giving LLM an App Store — which can browse, discover, and use any available tool on its own.
When to use Function Calling vs Model Context Protocol (MCP) ?
Function Calling to be used when:
- We require simple tools
- We control the whole application stack
- We’re using cloud models like ChatGPT or Gemini
- We want tight integration in our application logic
MCP to be used when:
- We’re building agentic systems
- We want tools to be modular, reusable, discoverable
- We want local LLMs (like Ollama, Gemma, Mistral) to use external data
- We want scaling to multiple tools or toolchains