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457,697 tools. Updated 2026-08-14 14:19

"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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  • 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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  • 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; always allowed.
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  • Find literal occurrences of an exact string in the corpus — article numbers, regulation ids, precise wording (e.g. "Article 8(3)", "2024/1781"). Complements semantic `search`: use this when you need the exact string, not the concept. Returns one match per (file, page) with an occurrence count and a text snippet; read the full page with `fetch`. Scans the literal chunk text of both indexes the semantic search serves (the two chunkings differ, so some passages exist in only one); the synthetic contextual enrichment is NOT scanned — it is not document text.
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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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  • 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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  • Search the web via Aimnis. Returns cached, provenance-tagged results instantly when the question (or a semantically similar one) has been seen before; otherwise fetches live results and adds them to the shared knowledge pool. Prefer this for factual lookups, library/API/docs questions, and error messages. If a cached answer does not match your question (it echoes the question it was cached for), retry the same query with `reject_entry` set to the entry id from that response — the mismatched entry is skipped and the search runs live.
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  • Rewrite one segment's creative direction from feedback ("make this shot a close-up", "show the machine from above") — an LLM rewrites the shot's prompts; continuation links, SFX, and overlays are preserved. The visual assets reset to not_started: re-render them afterwards (generate_segments or regenerate_segment_asset). For a precise prompt tweak with no rewrite, use regenerate_segment_asset with editable_sections instead.
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  • Ask when the question is general, cross-domain, or 'what is happening today'. Ranked search over 40 curated news sources, polled on every ingest run. For a domain-specific question (a filing, a CVE, a court ruling, an outage) prefer query_wire — a domain wire is always more precise than keyword search over news. Keyword search, not semantic: send distinctive terms. PAID: this returns the price and the payment steps, not items. Buy the call over HTTP with x402 to get results.
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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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  • Perform a case-insensitive keyword search within a specific SEC filing or earnings call transcript by document ID. Returns matching lines with surrounding context and line numbers, making it ideal for finding exact terms, figures, or phrases that semantic search might miss. Typographic punctuation is folded before matching, so a plain-ASCII keyword (e.g. "world's") matches the smart punctuation stored in filings. The header reports the total number of matching lines even when only the first ones are shown. Use this after ListCompanyDocuments to locate precise occurrences of a keyword (e.g., a revenue figure, risk factor term, or executive name) within a known document. Complements semantic search tools by providing exact text matches rather than meaning-based results. Use ReadDocumentLines to read broader sections around matches.
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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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  • Create a short-lived current precise-location request with a browser fallback. Once approved, it can authorize local category/recommendation reach and Sponsored exposure. Wait poll_after_seconds, then call get_location_handoff once so the authorized customer phone can complete it automatically. Only if that poll is still awaiting_user should you show or send location_url. The assistant receives readiness and normal business results, never latitude or longitude. Requires an active database-backed customer personal-agent key.
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  • Check whether the customer approved current precise-location authority. This returns only status and expiry; it never returns coordinates. Once ready, pass handoff_id as location_handoff_id to search_businesses, search_category, or recommend_businesses.
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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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