MCP
Model Context Protocol is a standard way for AI applications to connect with external tools, data, and context servers.
Use MCP when reusable tool and context integrations matter across clients; use direct function calling for a narrow in-app MVP.
Use when
- Shared tool integrations
- Developer-facing AI products
- Context servers reused across clients
- Standardized tool access
Avoid when
- One-off internal prototypes
- A single known API call
- Products without external tool needs
- Flows that need only structured output
What MCP is for
MCP is useful when tool and context integrations should be reusable rather than hard-coded into one client. It gives AI applications a more standard way to discover and interact with external capabilities.
For a small product with one known API, direct function calling is often faster. MCP becomes more attractive when integrations multiply or when other clients should reuse the same context server.
Decision boundary
Use direct function calling for a narrow MVP. Consider MCP when the integration surface becomes a product capability, not just an implementation detail.
Common mistakes
- Adding MCP before there is a real integration problem.
- Treating MCP as a replacement for permission design.
- Confusing protocol adoption with product value.
Next decision
Compare MCP with function calling. If you are building an agent product, also map tool calling, evaluation, and guardrails.