AI comparison
MCP vs tool calling
Tool calling is how a model asks for a function to be run; MCP is a standard for offering such tools from a separate server that many AI applications can share.
| Aspect | MCP | Tool calling |
|---|---|---|
| What it is | A protocol between an AI application and a tool server | A feature of a model API |
| Where tools live | In an MCP server, possibly another process or machine | In your own application code |
| Reuse | One server works with any MCP client | Tools are defined per application |
| Extra work | Run and secure a server | None beyond your own code |
Choose MCP when
- Several assistants or teams should share the same tools
- You want to offer your system to AI applications you do not write
- Tools need their own process, permissions or deployment
Choose Tool calling when
- One application with a few tools of its own
- You want the fewest moving parts
- You are learning how a model uses tools at all
Learn it hands-on
These free lessons teach it with commands to run in a lab and checked tasks. Read them whole with no account.
Related terms
- Model Context Protocol (MCP)
- The Model Context Protocol is an open standard for connecting AI applications to tools and data, so one server can serve any assistant that speaks it.
- Tool calling
- Tool calling lets a language model ask your program to run a named function with arguments, such as a search or a lookup, and then use the result.
- JSON Schema
- JSON Schema is a standard way to describe the shape of JSON data, its fields, types and required values, so it can be checked automatically.