Short answer: AI answers help a reader ask questions over the docs, which is a real feature for a large prose library. A spec-driven studio takes a different route: it generates an MCP server so an external agent can call the API correctly, not just answer questions about it. The first reads the docs; the second acts on the API.
What in-docs answers solve
Navigation and comprehension at scale: a reader asks a question and gets an answer grounded in the documentation instead of searching. That is valuable when the docs are large.
What an MCP server solves
| Capability | AI answers | MCP server |
|---|---|---|
| Answers questions | Yes | Indirectly |
| Calls the API | No | Yes |
| Grounding | Docs | Contract |
| Acts for the user | No | Yes |
Why the distinction matters
An answer is informational; a tool call changes state. A generated MCP server lets an agent perform the operation correctly, which is a different capability from explaining it.
How they fit
Use AI answers for the readers and an MCP server for the agents. They serve two audiences and can coexist.
This is part of the Powerduck vs Mintlify comparison. Next, read export vs lock-in.
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