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search_works

Search AO3 for fanfiction works using filters like fandom, relationship, tags, rating, word count, and more. Combine filters freely and use wildcards for broader results.

Instructions

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: "KazuhaScaramouche*" 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
tagsNo
pagesNo
queryNo
titleNo
authorNo
fandomNo
ratingNo
sort_byNorelevance
characterNo
categoriesNo
word_countNo
relationshipNo
complete_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Discloses that search is cheap and reading is delegated, that query is full-text and narrows hard, that filters multiply and can cut to zero, and that results include numeric ids and k/h ratio. Also explains wildcard behavior and recovery steps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear headings (RECOMMENDATION WORKFLOW, SEARCH STRATEGY, etc.) and bullet points. However, the description is quite long; while every part is valuable, it could be condensed slightly without losing meaning. Still, the structure aids readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (14 parameters, no annotations, and an output schema exists that covers return values), the description is fully complete. It covers search behavior, error recovery, output interpretation, and links to sibling tools. No gaps remain for an agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds extensive meaning for every parameter. For example, query lists operator syntax, warns against stuffing synonyms; fandom explains exact-match pitfalls and wildcard use; tags lists popular canonical tags. Each parameter is given detailed guidance, far beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Starts with 'Search AO3 for works' — a specific verb+resource. Distinguishes from siblings read_works and find_tags, and explains the recommendation workflow where this tool is for initial search and shortlisting.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use (for broad search to over-fetch) and when-not-to (to read fics — delegated to read_works). Gives comprehensive strategy: use wildcards, recover from 0 results by broadening queries, and contrast with find_tags. Also advises on recommended pages for recommendation hunts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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