feat(phase7): grow corpus, MCP write-tools+auth, /metrics observability

- content/: +5 real docs (caddy, bullmq, mcp-streamable-http, postgres-fts,
  cloudflare-525 writeup) across docs/writeups/notes. Reindexed: 9 docs, 17
  chunks, 5 revisions (was 4 docs).
- apps/mcp: add write-tools index_document/reindex_all/restore_revision (require
  x-webhook-secret) + queue_status (public). createMcpServer(authSecret?) threads
  the HTTP header; stdio keeps writes open (trusted local).
- apps/api: GET /metrics (Prometheus text: uptime + queue job gauges).
- Caddy: expose /metrics on wiki. domain -> :4020.
Verified live: /metrics 200; MCP tools/list -> 10; index_document unauth -> error,
auth -> enqueues + worker drains; typecheck green.

content/notes/postgres/full-text-search.md

@@ -0,0 +1,59 @@
+---
+id: postgres-full-text-search
+title: PostgreSQL Full-Text Search
+type: documentation
+tags:
+  - postgres
+  - fts
+  - tsvector
+  - search
+status: published
+author: asep
+created_at: 2026-08-20
+updated_at: 2026-08-20
+---
+
+# PostgreSQL Full-Text Search
+
+MCPedia's keyword search is backed by PostgreSQL's native full-text search (FTS), not
+an external engine. The `documents` table carries a generated `tsvector` column that
+combines the title (weight `A`) and body (weight `B`).
+
+## Generated search vector
+
+The column is `generatedAlwaysAs`, so it is always consistent with the row and needs
+no trigger:
+
+```ts
+searchVector: tsvector("search_vector")
+  .notNull()
+  .generatedAlwaysAs(
+    sql`setweight(to_tsvector('simple', coalesce(${documents.title}, '')), 'A') ||
+        setweight(to_tsvector('simple', coalesce(${documents.body}, '')), 'B')`,
+  ),
+```
+
+The `'simple'` config disables stemming, so mixed identifier/English queries (e.g.
+`websocket`, `tsvector`) match literally. A GIN index on `searchVector` keeps lookups
+fast.
+
+## Query + ranking
+
+Search parses the user query with `websearch_to_tsquery` (or `plainto_tsquery`), then
+ranks with `ts_rank`:
+
+```sql
+SELECT *, ts_rank(search_vector, q) AS rank
+FROM documents, websearch_to_tsquery('simple', $1) q
+WHERE search_vector @@ q
+ORDER BY rank DESC;
+```
+
+A headline snippet for the UI comes from `ts_headline`, which bolds the matched lexemes.
+
+## Hybrid fusion
+
+Semantic search (embedding cosine) and FTS are fused with **Reciprocal Rank Fusion**
+(RRF) in `packages/search`. Each result set is ranked, scored `1/(k + rank)`, and the
+summed scores re-rank the union — no cross-score normalization needed, which is robust
+when the two signals live on different scales.