Open Museum MCP vs Ao3 MCP

Federated museum and cultural-collection search

Strict default-deny rights verification

Normalized artwork metadata

Inclusive date-range filtering

Deterministic citation formats

Region and dynasty-aware discovery

search_works

Search AO3 for works. All filters optional; combine freely. RECOMMENDATION WORKFLOW — reading before recommending is MANDATORY, and the reading is done by a SEPARATE model, not you. Blurbs are author-written ads; never recommend, rank, or summarize a fic from its blurb alone. Cast a wide net (pages=2-3, i.e. 40-60 blurbs), shortlist the promising ones, then hand the top ≤20 ids to `read_works` — a second AI reads them and reports back. Recommend ONLY fics that came back from `read_works`. Do not read fic text yourself; delegating it is the entire point of this server. SEARCH STRATEGY — searching is cheap and reading is delegated, so the winning move is always to OVER-FETCH and let `read_works` brute-force the shortlist, never to craft one perfect narrow query. Filters multiply: each one you add cuts the pool, and stacked filters routinely cut it to zero. USE WILDCARDS LIBERALLY — abuse them. A `*` matches any run of characters and works in EVERY name field (`fandom`, `relationship`, `character`, `tags`) and in `query`. Wrapping a term in stars is the single best defence against AO3's exact-canonical-name trap: `fandom="Genshin Impact (Video Game)"` returns ZERO (the canonical tag is actually "原神 | Genshin Impact (Video Game)"), but `fandom="*Genshin Impact*"` returns the whole fandom. Likewise `relationship="*Kazuha*Scaramouche*"`, `tags="*Enemies to Lovers*"`. When you don't know the exact canonical name — which is most of the time — reach for a wildcard first instead of guessing the literal string. IF YOU GET 0 (or few) RESULTS, that is almost always your query being too narrow, NOT the content missing from AO3. Recover instead of giving up: - FIRST, wildcard the name fields (`*Genshin Impact*`). This fixes the most common cause — an exact-match field that didn't match the canonical tag — in one retry, without a separate `find_tags` round-trip. - Still unsure of a name? `find_tags` resolves it, or move the idea into `query` as free text (fuzzy, no canonical spelling needed). - Drop filters one at a time and retry: `word_count` first, then `complete_only`, then `rating`. Re-add only what the user insisted on. - Concepts don't need to be tags at all: "slow burn rivals in a bakery" works fine as free-text `query` even if no such tag exists. - Still thin? Search the broad version (fandom + category, sort by kudos), fetch 2-3 pages, and let the blurbs + `read_works` do the filtering. A human reader has to search narrowly because they can only read a few fics; you can read twenty at once, so breadth costs you nothing. Results show numeric work ids, not URLs. When relaying a work to the user, build the link yourself: https://archiveofourown.org/works/{id} Each result shows a kudos-to-hits ratio (k/h) — AO3's most honest quality proxy, since kudos are one-per-reader but hits count every visit. Compare it only within similar works: multi-chapter fics accumulate hits on every chapter visit, so long WIPs run structurally lower ratios than one-shots. Args: query: free-text search. Supports AO3's full operator syntax (case-sensitive, space after colon required where shown): `"exact phrase"`, `AND` / `OR` / `NOT`, `-term` to exclude; `words>10000`, `words:1000-5000`, `kudos>500` (same for hits/ comments/bookmarks); `sort:kudos`, `sort:hits`, `sort:>posted` (oldest first); `otp: true` (exactly one ship, no side pairings); `creators: username` / `-creators: username`; `summary: "phrase"`; `expected_number_of_chapters: 1` (one-shots only); `series.title: *` (part of a series); `language_id: en`. Also supports `*` wildcards, e.g. `*coffee shop*`. ⚠️ query is a FULL-TEXT match on the fic body, AND'd with every other filter — so it narrows HARD. Do NOT stuff mood/concept synonyms here ("nuzzle OR forehead kiss OR won't let go"): that demands the prose literally contain one of those strings on top of your tag/fandom filters, and routinely collapses a healthy 60-result search to 0. Concepts belong in `tags` (wildcarded), not here. Use query for author names, quoted title/summary phrases, or the numeric operators above — leave it EMPTY when a tag already covers the vibe. title: words in the work title. author: author/creator name. fandom: fandom name, e.g. "Naruto" (comma-separate several). Exact canonical match — but `*` wildcards work here: prefer "*Genshin Impact*" over the literal name to survive canonical tags with prefixes/aliases (e.g. "原神 | Genshin Impact (Video Game)"). relationship: ship tag. Format: "A/B" romantic, "A & B" platonic, canonical name order, e.g. "Kakashi Hatake/Iruka Umino". Wildcards work: "*Kazuha*Scaramouche*" beats guessing the exact tag order. character: character name(s), comma-separated. Wildcards work here too. tags: freeform tags, comma-separated, EXACT canonical spelling (use find_tags to resolve, or wildcard it: "*Enemies to Lovers*"). Popular canonical tags: Fluff; Angst; Hurt/Comfort; Emotional Hurt/Comfort; Angst with a Happy Ending; Hurt No Comfort; Enemies to Lovers; Friends to Lovers; Enemies to Friends to Lovers; Slow Burn; Mutual Pining; Fake/Pretend Relationship; There Was Only One Bed; Idiots in Love; Getting Together; Established Relationship; First Kiss; Found Family; Fix-It; Time Travel; Kid Fic; Domestic Fluff; Tooth-Rotting Fluff; Crack; Crack Treated Seriously; 5+1 Things; POV Outsider; Soulmates; Smut; Plot What Plot/Porn Without Plot; Alpha/Beta/Omega Dynamics; Dead Dove: Do Not Eat; Canon Compliant; Post-Canon; Alternate Universe - Modern Setting; Alternate Universe - Canon Divergence; Alternate Universe - Coffee Shops & Cafés; Alternate Universe - College/University; Alternate Universe - Soulmates. rating: one of: general, teen, mature, explicit, not rated. categories: comma-separated relationship categories to include: F/F, F/M, Gen, M/M, Multi, Other. Empty = all. complete_only: only finished works. word_count: range like "10000-50000", ">5000" or "<20000". sort_by: relevance | kudos | hits | comments | bookmarks | words | date_updated | date_posted. page: which result page to start from (for paging through results). pages: result pages to fetch, 20 works each (1-5). For a targeted lookup 1 is enough; for a recommendation hunt fetch 2-3 pages (40-60 blurbs) so the read_works shortlist has real competition.