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524,103 tools. Updated 2026-09-06 14:24

"Precise search" matching MCP tools:

  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • GET /search — Cross-resource omni-search Cross-resource search across profiles, rooms, messages (incl. private DMs + group DMs you're in), events, and chapters in one round trip. Returns the top-N matches per resource, grouped by resource. Use this when you don't yet know which resource carries the answer — agents typically call this first, then drill into a specific `GET /search/<resource>` for more depth on a single bucket. There's no page param: when you hit the per-resource limit and want more, switch to the per-resource endpoint for that one. The events slice has a baked-in forward-looking default (events ending in the last 30 days or later, and currently enabled) — this matches the in-app "Search across DC" surface. Use `GET /search/events` directly to look further back in time. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.
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  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Resolve a name or prefix to APITube taxonomy IDs so you can use them in the `search_news` tool's filters. The `search_news` tool's most precise filters require IDs you cannot guess: - entity.id — numeric (persons, organizations, locations, brands, events...) - category.id — IPTC slug, e.g. "medtop:04000000" - topic.id — slug - industry.id — numeric Use this tool FIRST to look those IDs up, then pass them into `search_news`. USAGE: - type: which taxonomy to autocomplete — one of "entities", "categories", "topics", "industries". - prefix: the name (or its beginning) to search, e.g. "Tesla", "Elon", "spo". Returns an array of matches; take the `id` of the best match and put it into `search_news`: suggest({ type: "entities", prefix: "Tesla" }) → [{ id: 12345, name: "Tesla, Inc.", ... }] search_news({ entity: { id: "12345" } })
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  • Deterministic IBAN check before a SEPA/international transfer: format regex, per-country length (public SWIFT registry, ~85 countries) and ISO 7064 mod-97 checksum. Query: ?iban=FR1420041010050500013M02606 (spaces/dashes tolerated). Returns valid, country, bban, and a precise failure reason. Pure offline computation, 1y cache. Price: $0.001 USDC per call (x402).
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  • Search Vascue's public healthcare-ops, insurance-claims and booking docs. Public content only.

  • Search Vascue's public healthcare-operations, insurance-claims and clinic-booking documentation. Public content only; never send patient data, credentials or booking requests.

  • Return the values that actually exist in the catalogue for filtering a search: sizes, conditions, source platforms, artists, designers, and the price range. Use this before search_pieces when you want to build a precise query from real values rather than guesses. For example, to check which sizes of a garment are genuinely listed right now, or which platforms currently carry a given collection.
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  • Search US nonprofits by name with optional state filter. Read-only. No side effects. Idempotent. US only. Returns up to 25 matches. name: Full or partial organisation name. Required. state: Two-letter US state code e.g. CA, NY. Optional, defaults to all states. Returns EIN, name, state, revenue, and NTEE code for each match. Use this when you have a name but not the EIN. Use nonprofit_fetch_nonprofit_by_ein instead when you have the exact EIN for a precise single lookup. Verified source: IRS EO BMF. 7-day cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="nonprofit_search_nonprofits_by_name", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".
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  • Raw ChEMBL mechanism records for one exact molecule ChEMBL ID (e.g. "CHEMBL1703"), returned verbatim from the API. Use when you already hold the precise molecule ID and want the unshaped rows; to start from a drug name, call `chembl_mechanism`.
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  • Search one SEC filing or earnings-call transcript by document ID. semantic mode uses hybrid relevance and returns excerpts in document order with approximate line numbers. exact mode performs a literal case-insensitive substring match and returns precise matching lines. Get document IDs from SearchDocuments or ListFilings; use ReadDocumentLines for surrounding text.
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  • Search the Klever VM knowledge base for smart contract development context. Returns structured JSON with matching entries, scores, and pagination. Use this for precise filtering by type or tags; use search_documentation for human-readable "how do I..." answers.
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  • Low-friction inventory health estimate for Shopify merchants. Use this when the merchant doesn't have precise inventory figures — requires only monthly revenue, SKU count, and industry segment. Inventory value and dead stock are estimated from industry benchmarks; all assumptions are returned transparently. Returns a 0–100 health score, risk flags, plain-language diagnosis, and prioritised recommended actions. Ideal for AI-assisted lead qualification and first-contact diagnostics. For a precise score using actual inventory figures, use inventory_health_score instead.
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  • Search the web Search the web. Two modes governed by ``scrapeOptions``. - **Omit ``scrapeOptions``** → SERP-only: returns the search engine's raw snippets (``url`` + ``meta`` with ``title`` / ``description`` / ``source`` / ``publishedAt`` / ``imageUrl*``). No per-page fetch, fast and cheap. - **Pass ``scrapeOptions: {}``** → deep-scrape every result, return page-faithful Markdown under ``markdown``. - **Pass ``scrapeOptions: {"format": "json"}``** → deep-scrape every result, return the structured page summary under ``json`` (same shape as ``/webtools/scrape``'s ``json`` field). In deep-scrape mode, results where the chosen format produced no content are dropped from the response, so the response may hold fewer than ``limit`` results. ``meta.statusCode`` carries the fetched page's HTTP status when deep-scraped. ``query`` is compatible with common Google search-operator syntax: ``site:``, ``intitle:``, ``filetype:``, ``"exact phrase"``, ``-exclude``. To filter by whole domains, prefer the structured ``includeDomains`` / ``excludeDomains`` — they are folded into the matching ``site:`` / ``-site:`` operators (and may be combined, e.g. include a parent domain while excluding one subdomain). Use ``sources`` to pick the result bucket — ``"web"`` (default), ``"news"``, or ``"images"`` (combinable); ``tbs`` for a time filter (``qdr:d`` / ``qdr:w`` / ``qdr:m`` / ``qdr:y``); ``limit`` (1-20, default 10) to cap results. Billing scales with the number of results returned, with a minimum of 1 credit per call (an empty result set still bills the minimum). ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "success": true, "meta": { "requestId": "Requestid", "timestamp": "Timestamp" } } ``` **Output Schema:** ```json { "properties": { "success": { "type": "boolean", "title": "Success", "description": "Whether the request was successful", "default": true }, "data": { "description": "Response data payload" }, "error": { "description": "Error details if request failed" }, "meta": { "description": "Metadata for API responses.\n\nCredit fields follow the ADR-0003 parallel-fields strategy (Option 3):\n- `credits_remaining` / `credits_consumed` (int): legacy fields, rounded\n to whole credits, kept for zero-breaking-change to existing SDK clients.\n- `credits_remaining_exact` / `credits_consumed_exact` (float): new\n precision-aware fields for clients that opt in to decimal credits.\n\nSee ADR-0003 decision 5 and the \u00a78 deprecation timeline.\n\nTODO(2026-11, ADR-0003 \u00a78 +6mo): mark `credits_remaining` /\n`credits_consumed` as `deprecated=True` in their Field() definitions\nand announce in customer changelog.\nTODO(2027-05, ADR-0003 \u00a78 +12mo): remove the legacy int fields via a\nmajor-version bump of the OpenAPI surface.", "properties": { "requestId": { "type": "string", "title": "Requestid", "description": "Unique request identifier" }, "timestamp": { "type": "string", "title": "Timestamp", "description": "Response timestamp in ISO 8601 format" }, "total": { "title": "Total", "description": "Total number of records" }, "page": { "title": "Page", "description": "Current page number" }, "pageSize": { "title": "Pagesize", "description": "Number of records per page" }, "totalPages": { "title": "Totalpages", "description": "Total number of pages" }, "creditsRemaining": { "title": "Creditsremaining", "description": "Remaining API credits (rounded to whole credits; see creditsRemainingExact for precise value)" }, "creditsConsumed": { "title": "Creditsconsumed", "description": "Credits consumed by this request (rounded; see creditsConsumedExact for precise value)" }, "creditsRemainingExact": { "title": "Creditsremainingexact", "description": "Remaining API credits, precise to 1 decimal place" }, "creditsConsumedExact": { "title": "Creditsconsumedexact", "description": "Credits consumed by this request, precise to 1 decimal place" }, "tokensUsage": { "description": "Provider token-usage block \u2014 populated on terminal video polls only, null on every non-video endpoint. See TokensUsage for its fields." } }, "type": "object", "required": [ "requestId", "timestamp" ], "title": "ResponseMeta" } }, "type": "object", "required": [ "meta" ], "title": "OpenApiResponse[CrawlerSearch]", "examples": [] } ``` **422**: Validation Error Content-Type: application/json **Example Response:** ```json { "detail": [ { "loc": [], "msg": "Message", "type": "Error Type", "ctx": {} } ] } ``` **Output Schema:** ```json { "properties": { "detail": { "items": { "properties": { "loc": { "items": {}, "type": "array", "title": "Location" }, "msg": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Error Type" }, "input": { "title": "Input" }, "ctx": { "type": "object", "title": "Context" } }, "type": "object", "required": [ "loc", "msg", "type" ], "title": "ValidationError" }, "type": "array", "title": "Detail" } }, "type": "object", "title": "HTTPValidationError" } ```
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  • SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.
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  • SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.
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  • Get a single release cycle's support details for a product — release date, EOL, active-support end, latest patch, LTS, and any extended-support window. Use for a precise version question like "when does Python 3.9 lose support?". `product` is a slug from list_products; `cycle` is a version like "3.12", "20.04", "18". Keyless.
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  • Search notes by query. Returns snippets with a heading breadcrumb (title > section > subsection) that locates the approximate section, plus a precise toc_path per match. Each result carries note_path (string) and note_id (integer); each match carries match_id (string, form "p<pid>:c<chunk>"). Drill-down workflow: 1) search to find the approximate section via the breadcrumb; 2) call note_html(path=<result.note_path>, toc_path=[...]) to read the matched section, or expand(path=<result.note_path>, toc_path=[...]) to navigate the note's structure level by level; 3) note_html(path=<result.note_path>, match_id=<match.match_id>) for a focused chunk window. Each match also carries section_url — a link straight to that heading, for citing the section rather than the whole note.
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  • Check whether an address sits inside our Washington coverage and resolves precisely enough to book. Use when the customer wants an area answer without searching slots — search_availability already validates the area itself. Accepts: `address` (e.g. a Seattle street or ZIP) and optional `service` — notary or apostille rules differ. Returns withinServiceArea, distanceMiles, precise; if the address cannot be resolved the message says not found.
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  • Semantic (meaning-based) search over Teppek's live listings, backed by a multilingual embedding index. Use it when the user describes what they want in natural language rather than exact keywords — e.g. "outdoor jobs that don't need a degree", "affordable family cars that are good in snow", "a cosy studio close to the university". Returns the closest-matching ACTIVE listings ranked by semantic similarity (each item carries a `score`). Complements search_listings: prefer search_listings for precise keyword/role/country/category/price filtering and exact counts; prefer semantic_search when meaning, synonyms, or fuzzy intent matter more than literal terms. Optional `vertical` narrows to jobs/real_estate/vehicle/service.
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  • Runs a draft seed document through the exact parser the Calaf app imports with. Pass the entire JSON document as a string. Returns per-section counts and the precise issue list (path, severity, message): severity "error" means nothing imports; "dropped" means that row or field is discarded and the rest lands. Fix the issues and validate again until clean before delivering the file to the user.
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  • Fetch one stored value by its exact key. When to use: Call when you know the key you wrote earlier. Cheaper and more precise than memory.search. Price: US$0.000500 per call.
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