Agent report-back: structured summary after MCP-driven course/deck builds

**From the same 2026-09-15 strategy conversation as #1 (agents-in-tow as the primary market). If a user's agent is doing the building, the user needs a way to see what it did without reading every tool call.**

## The idea

Right now `build_a_course` (`packages/mcp/src/prompts/build-a-course.ts`) guides a connected AI through clarifying a goal, grounding it in real sources, building a syllabus, and authoring cards — but there's no structured output at the end that the agent hands back to the human. The agent just stops after the last tool call.

Add a "summary" step/tool to the course-building (and possibly deck-building) flow that produces something like: what was built (course/deck names, card counts, difficulty progression), what sources it was grounded in, and why it made the structural choices it made (e.g. "deck 2 gates on deck 1 because X is a prerequisite for Y"). This becomes the trust layer for a user who didn't watch the process happen live.

## Why this matters

The whole pitch of "AI builds this for you" only works if the user can verify what got built without re-reading every card themselves. A course showing up in the library with zero explanation is a worse experience than one accompanied by a short rationale the agent can just paste into chat.

## Decisions to make

- **New MCP tool vs. a documented expectation in the prompt text?** E.g. a `summarize_course` tool that reads back structured data for the agent to narrate, vs. just instructing `build_a_course` to compose a summary itself from what it already knows.
- **Does this apply narrowly to Courses, or generally to any MCP-driven content creation** (a single deck, `add_cards` on an existing deck, etc.)?
- **Where does the summary live, if anywhere** — is it purely conversational (the agent just tells the user), or should something persist on the course/deck record itself (e.g. a "how this was built" note visible in the web/Android UI too)?

## Reuse

Builds on the existing `build_a_course` prompt and Courses data model (`courses`, `course_decks` tables) — this is additive, no schema changes obviously required unless the "persist a rationale" option is chosen.

Start by reading packages/mcp/src/prompts/build-a-course.ts and reviewing the courses and course_decks data model described in the issue. Decide whether the summary is conversational or persisted, whether it covers courses and decks, and whether it needs a new MCP tool; done means the chosen flow produces the agreed structured summary for the agent and user.

Written by the indexing model from the issue text.