closes CODAGT-203
## Summary
`list_templates` now returns a ranked shortlist with a recommendation,
so the chat agent can pick the right template the way a colleague would:
prefer what matches the request, what the user already uses, and what
the rest of the organization uses. Instead of teaching the model an enum
protocol in prompts, every result carries a fixed `next_step`
instruction telling the agent what to do.
## How list_templates works
1. **Fetch**: active, non-deprecated templates in the chat's
organization, filtered by the admin template allowlist, authorized as
the chat owner (no system escalation).
2. **Query relevance** (optional `query` argument): each template
receives the highest tier any of its fields matches, and a higher tier
always outranks a lower one regardless of usage:
| Tier | Match |
|------|-------|
| 4 | name or display name equals the query |
| 3 | name or display name starts with the query |
| 2 | name or display name contains the query |
| 1 | description contains the query (checked only when no name field
matched) |
| 0 | no match; the template is excluded |
Matching is case-insensitive and ignores spaces/hyphens/underscores
(`python gpu` matches `python-gpu`).
3. **Usage signals**: a new `GetTemplateRankingSignalsByOwnerID` query
returns, per template, the owner's active and recently-deleted workspace
counts within a 60-day window, the last in-window usage, and the count
of distinct developers with an active workspace (unclaimed prebuilds
excluded).
4. **Affinity score** (computed in Go, per template, from that
template's signals only):
```text
affinity = 10 x (active + 0.5 x deleted) x 0.5^(days_since_last_use /
14)
+ ln(1 + active_developers)
```
`active`/`deleted` are the owner's in-window workspace counts,
`days_since_last_use` is measured from the most recent in-window usage
(the personal term is zero without in-window usage), and
`active_developers` is the org-wide count. Personal usage carries 10x
the weight of org popularity; the confidence floor is the score of two
active developers (`ln 3`) and the required lead over the runner-up is
`ln 3 - ln 2`.
5. **Rank**: query tier first (when a query is present), then affinity
score, then name/ID for determinism. Results paginate 10 per page with
`next_page` present only when more exist.
## Recommendation contract
The result tells the agent what to do next instead of describing
confidence levels:
- `recommended_template_id` is present only when the top template is a
clear winner: the only available template, a decisive query match, or an
affinity score that clears a floor and leads the runner-up by a derived
margin.
- `next_step` is always present and is one of four fixed sentences: use
the recommendation, ask the user to choose, retry a query that matched
nothing, or report that no templates are available.
Per-template items carry raw evidence (`active_developers`,
`your_workspace_count`, `last_used_by_you`) rather than derived labels.
When signals fail to load, the tool logs and degrades to asking the user
unless the query alone is decisive.
Prompts and the `create_workspace`/`read_template` descriptions
reference the field through the `chattool.NextStepField` constant, so
the instruction lives in one place and cannot drift. `create_workspace`
remains idempotent and allowlist-enforced.
## Authorization
The signals query runs with the chat owner's permissions: reading the
owner's own workspaces plus a template-metadata read for the cross-user
popularity count. dbauthz rejects the call if any requested template is
not readable by the owner (covered by allow and deny method tests).
## Docs
Adds `docs/ai-coder/agents/tools/` explaining how agent tool calls work,
with `list_templates` ranking and the `next_step` contract as the first
documented tools.
Jaayden Halko
bc44cdda75