MCP (Model Context Protocol)
Why MCP Is Needed
Today, AI calling tools has different solutions here and there — every AI product defines its own tool protocol, and connecting to a database requires writing adapter code separately every time.
The result?
- The same tool needs to be integrated once by this app, and again by that app
- Tool descriptions, permission models, and result formats are all incompatible
- Ecosystem reuse is very difficult
MCP solves this layer of fragmentation.
What Is MCP
One-line definition: MCP (Model Context Protocol) is an open protocol that enables AI applications to connect to external capabilities in a unified way.
Analogy: Like the USB protocol — with a unified interface, mice, keyboards, and hard drives from any manufacturer work when plugged in. MCP is the "USB interface" for the AI world.
With it:
- Tool providers implement once, and any MCP-enabled client can reuse
- No need for every AI product to build its own private integration layer
This relates to Tool Calling as: Tool Calling is "the form in which models initiate actions," while MCP is "the standard for tool exposure and integration" — MCP enables tools to be called in a unified way via Tool Calling.
MCP's Three Core Primitives
| Primitive | Role | Analogy |
|---|---|---|
| Tools | Operations the model can call | A set of executable functions |
| Resources | Data the model or application can read | Read-only files or data sources |
| Prompts | Reusable prompt templates | Pre-built workflow entry points |
How to Do It: When to Use MCP
Scenarios suited for MCP:
- A tool wants to be reused by multiple AI clients
- Needing unified management of tool discovery, permissions, and calling methods
- Teams building Agents, IDEs, or internal assistant platforms that need a pluggable capability ecosystem
When not to use MCP:
- Temporarily connecting one internal API within a single project — writing Tool Calling directly is often simpler
Common misconceptions:
- MCP is not a model's own capability — it's a protocol between AI applications and external capabilities
- MCP doesn't equal Tool Calling: Tool Calling is the form of model-initiated actions, while MCP is the standard for tool exposure and integration
- Using MCP doesn't mean it's secure: permission control, sandbox isolation, and audit logging still need to be handled by the application side
Remember this: MCP is the "standard socket" for AI tool integration — implement once by tool providers, usable by multiple AI clients.
Related terms: Tool Calling · Function Schema
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