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741,549 tools. Updated 2026-11-04 06:30

"Search for 'sequ' - possibly sequence-related queries or prefixes" matching MCP tools:

  • Search for products on Kapruka.com by keyword, with optional category filter and pagination. Returns a ranked list of matching products with prices, stock status, images, and URLs, plus the category facets available for the query (facets.categories) — the names that work in `category` to narrow the same search. Supports cursor-based pagination — pass next_cursor from one response into the next call. Pagination is capped at 3 pages per query to discourage catalog enumeration; for broader discovery, refine the query or narrow with a facet instead. Queries must be at least 3 characters and contain specific terms — pure stopword queries (e.g. "the", "a an") are rejected. Relevance: the API matches ANY single query word, so extra words add loosely related results rather than narrowing. Search the thing the customer wants (the head noun) and check the product names actually contain it before presenting them. Prices and min_price/max_price are both in `currency` — pass the customer's own budget in their own currency; do not convert it. Gifts for recipients OUTSIDE Sri Lanka are not in this search: use kapruka_gift_countries / kapruka_gift_country_products. A row's `price` is the discounted price checkout charges (the bounds filter on it); `compare_at_price` is set when on sale. Search results carry NO delivery-scope information. Food, hotel cakes and liquor are delivered only to selected cities — never infer deliverability from a search hit. Before quoting delivery on a specific item, call kapruka_get_product (read `delivery.island_wide`) or kapruka_check_delivery with `product_id`. Args: params (SearchProductsInput): - q (str): Search query (e.g. 'birthday cake', 'roses', 'tea gift'). Min 3 chars. - category (Optional[str]): A facet name from a previous search's facets.categories - limit (int): Results per page, 1–50 (default 10) - cursor (Optional[str]): Pagination cursor from previous response - currency (str): LKR (default), USD, GBP, AUD, EUR - min_price (Optional[float]): Min price (inclusive) in `currency` - max_price (Optional[float]): Max price (inclusive) in `currency` - in_stock_only (bool): Restrict to in-stock items (default false) - sort (str): 'relevance' | 'price_asc' | 'price_desc' | 'newest' | 'bestseller' - include_stubs (bool): Deprecated, no effect - response_format (str): 'markdown' (default) or 'json' Returns: str: Search results in the requested format. JSON schema: { "results": [ { "id": str, "type": "product", "name": str, "summary": str, "price": {"amount": float | null, "currency": str}, "compare_at_price": {"amount": float, "currency": str} | null, "in_stock": bool, "stock_level": str, "image_url": str | null, "category": {"id": str, "name": str, "slug": str}, "rating": null, "ships_internationally": bool, "url": str } ], "next_cursor": str | null, # null after page 3 even if upstream has more "total_estimate": int, # index hits for the query words (any-word match) "applied_filters": {"q": str, "category": str, "currency": str, ...}, "facets": {"categories": [{"name": str, "count": int}]}, "category_filter_dropped": str, # only when `category` was not a facet here "valid_categories": [str] # ...and the facet names that are } Error: "Error: <message>" or "No products found for '<query>'" on failure.
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  • Store a human-pasted sequence and return a handle to re-use it. For a sequence you've already pasted into the conversation, this gives back a short handle so you can run several tasks on it without re-pasting the bases in each predict_* call. Note that the full sequence still passes through the LLM on THIS call — it does not save context on its own. For large sequences, prefer fetch_ensembl_sequence / fetch_gene_for_expression / load_local_fasta, which acquire the bases server-side and never round-trip them. A line-wrapped FASTA *body* may be pasted verbatim: whitespace is stripped before storing, so the handle's `length` counts bases and a later `tss_index` counts into the same string the API measures. (A FASTA `>` header line is not a sequence and is rejected by the API's alphabet check.)
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  • Every prefix currently announced with this AS as origin, with AS path and when each route was first seen. family: v4, v6 or both (default both). Large networks are truncated at 2,000 rows per family. Originated only: for prefixes the AS merely transits use pwhois_asn_transit; for counts without the list use pwhois_asn_info. Costs 1 query per family.
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  • Compare what this account pays for in Google Ads search with what its Search Console site ranks for, over the same dates: paid terms ranking top three, queries with no Google Ads search term found, and paid terms with no organic ranking, with cost, clicks and position. Identical wording only. Read-only; needs Search Console. Tools revision: 8ebdf5be.
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  • FREE search for paid API endpoints (x402) by task. Pass either a natural-language query (the planner will split it) or pre-split queries[]. Returns a structured plan with up to `maxRoutesPerQuery` candidates per sub-query. No auth. Limits: burst 200/10s, total 1000/15min per IP; free probes 10/h per IP, 20/h per agent. IMPORTANT: Do not fetch `route.url` directly. To execute the preferred route, call `call_endpoint({ routeId, arguments })` - `routeId` from `plan[].routes[].routeId`, `arguments` keys = `parameters[].name` of that route.
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  • Search SaidItHere for real-world software dilemmas, framework gotchas, tool fatigue, and battle-tested community consensus workarounds from Reddit, Hacker News, GitHub, and Stack Overflow. Usage Guidelines: - Primary discovery tool: use FIRST when investigating bugs, library gotchas, unexpected price hikes, or developer complaints by keywords. - Sibling routing: follow up with 'get_problem(slug)' using the discovered dilemma slug to inspect full evidence quotes, sources, and step-by-step workarounds. - Sibling routing: if 0 matching dilemmas are returned, use 'suggest_dilemma' to queue the topic for crawler consensus synthesis. - Do NOT use for generic language syntax lookups or textbook documentation queries. Behavior & Side Effects: - Pure read-only, safe, and idempotent query without side effects. Does not modify user, session, or database state.
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  • Oregon DMV MCP — live wait times at all 60 Oregon DMV field offices, plus the office locator the…

  • Crypto scam checks (tokens, sites, wallets), chain reads, web page to Markdown, email checks.

  • Search Partle's product catalog by name or description. CRITICAL SEARCH INSTRUCTION: Reason from the job to the product class first, then search with a descriptive product phrase (e.g. including substrate, material, or size class). DO NOT blindly search using the user's raw conversational words. Transform questions like 'what do I need to attach a mirror to a brick wall?' into a product phrase like 'heavy duty masonry wall anchor'. Two distinct modes: - **Default (no flags)** — fast keyword search. ~100ms. Acts like a normal "dumb" search box: matches the literal words you typed against product names and descriptions, with stemming. Good for queries where the user knows the product's likely name ("BC547", "Arduino Uno", "Bosch drill"). Returns noisy/wrong results on cross-language or attribute queries ("compost bin" matches Spanish "composta", not real composters). - **`super_search=True`** — slow, high-quality. ~1–2s. Run when the user describes what they want rather than naming it: cross-language ("Schraubenzieher Set" → real screwdriver sets even without German catalog entries), attribute-style ("small metal part with a flat head"), or any case where the default returns junk. Embeds the query with voyage-3-large, takes the cosine top-50 over the corpus (with an exact-name precision boost for part numbers), then a cross-encoder reranks them. The two modes are mutually exclusive in practice — pick one based on whether the user knows the product's name or is describing it. Use this when the user asks to find a specific product or browse products matching a query. Prefer over `search_stores` when the intent is product-led ("find a drill") rather than store-led. Use `get_product` afterwards if the user wants full details for one specific result. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search term. In default mode, treated as keywords (each word matched against product text). In `super_search=True`, treated as a natural-language description. min_price: Lower bound on price in EUR. Omit for no lower bound. Null-priced rows are NOT excluded by this filter — pass `has_price=True` if you need only priced listings. max_price: Upper bound on price in EUR. Omit for no upper bound. Tip — narrow by budget: `min_price=10, max_price=50, sort_by="price_asc", has_price=True`. Products without a listed price (a large fraction of the scraped catalog) sort last under either price ordering and are kept in results unless `has_price` filters them out. tags: Comma-separated tag filter (e.g. "electronics,bluetooth"). Tags are AND-ed together. store_id: Restrict results to a single store. Use the integer `id` from `search_stores` results. sort_by: One of `price_asc`, `price_desc`, `name_asc`, `newest`, `oldest`. Omit to use the default search-relevance ranking. has_price: When True, exclude products without a listed price (~most of the scraped catalog). Use this for competitive pricing or budget-bounded shopping. When False, return only null-priced listings (rarely useful). Omit to include both. semantic: Legacy flag. Pure vector ordering, ~250ms. Mostly superseded by `super_search=True` (which uses the same vector retrieval plus a cross-encoder rerank for materially better ordering at the cost of another ~700ms). Keep using it only if you specifically want vector retrieval *without* the rerank. super_search: **Enable for natural-language / "describe what I want" queries.** ~1–2s. Embeds the query with voyage-3-large, takes the cosine top-50 (with a precision boost for exact-name matches like part numbers / SKUs), then a cross-encoder reranks them. Use whenever the user is describing rather than naming — cross-language ("Schraubenzieher Set"), attribute-style ("small black metal bracket"), or any case where the default keyword path returns junk. Don't combine with cheap browse-style queries where the user typed an exact product name — keyword default is faster there. On `relevance_score` here: better than the bi-encoder cosine, but still not a "did I find what the user wanted" gauge. Behavior to expect: gibberish or fully-off-topic queries cap around 0.35; loosely-related catalogue clusters can score 0.7+ even when no item truly matches (a "ceramic vase" query in a catalog with no vases but many ceramic flowerpots will still score high). **Read the product names** before claiming a match. The score is most useful as a relative signal within one result set — a sharp drop between rank N and N+1 marks where the catalog stops being useful for this query. limit: Max results (1–100, default 20). Larger limits are slower and consume rate budget faster. offset: Skip this many results before returning. Use for pagination (offset += limit on each follow-up call). Returns: A list of products. Each includes `id`, `name`, `price`, `currency`, `url`, `description`, `store` (id/name/address), `tags`, `images`, a canonical `partle_url`, and `relevance_score` (cosine similarity 0–1 between the query and the product's embedding when a query was provided; `None` otherwise). **Always share `partle_url` with the user so they can view the listing.** Caveat on `relevance_score`: it is monotonic *within a single search result set* (useful for spotting a big drop-off between rank 3 and rank 4), but its absolute value is not well-calibrated across queries — most results land in 0.55–0.80 regardless of whether the catalog has truly relevant items. Don't infer "this is a great match" from a 0.75 score alone.
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  • Predict a gene's expression from the sequence around its TSS. Expression is cell-type-specific, so `description` (cell type / assay context, e.g. 'K562 cell line') is REQUIRED — the API rejects requests without it. The input rule is the same for every expression model: at least 9,198 bp, with the TSS at least 4,599 bp from each end, up to 500,000 bp. Longer TSS-centred sequence gives better predictions: each model's bio_spec.recommended_flank_bp (see list_models) says how many bp it reads on each side of the TSS, and flank beyond that is ignored. Less flank, down to 4,599 bp, is accepted and scored, but the model then reads less than it was trained on. Omit `model` for the server's default. - A 9,198 bp sequence with the TSS at offset 4,599 (the midpoint) needs no `tss_index`. - Any longer sequence needs `tss_index`: the 0-based offset of the TSS into it (a fetch_gene_for_expression handle carries its own). The API cuts what the model reads around that offset and never scans for a TSS itself. Under 9,198 bp, or a TSS closer than 4,599 bp to either end, is rejected here and by the API (422). A locus with no offset is rejected too, because it is indistinguishable from a mis-centred window. A very N-rich or low-complexity window can also be refused by the API (422). An offset that is merely WRONG (e.g. counted over a wrapped FASTA's characters, or against a chromosome coordinate instead of an offset into THIS sequence) still succeeds and scores the wrong window — verify meta.task_specific_counts.scored_window in the response. Easiest paths: fetch_gene_for_expression(gene) already fetches a long TSS-centred handle (as much flank as the listed models recommend) and records its `tss_index`, which is sent for you when you pass that handle without one; find_genes_and_predict_expression takes a raw region and finds each TSS for you.
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  • Fetch the canonical amino-acid sequence (FASTA) for a UniProtKB accession, with length and the parsed header. Set include_isoforms to also return the alternatively-spliced isoform sequences. This is the cheap sequence-only path — for the full functional record use uniprot_get_entry. A merged accession returns the sequences of its merge target, labelled with the target accession and the requested one in mergedFrom; a demerged or deleted accession fails not_found naming its replacements or UniParc ID. Accessions come from uniprot_search_proteins or uniprot_map_ids; strip any "-N" isoform suffix (P04637-2 to P04637) before calling.
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  • WHEN: object name is unknown, partial, or you need to find by concept/keyword. Search the D365 F&O knowledge base for X++ code, tables, classes, forms, views, enums, EDTs, security objects using natural language or partial names. Returns ALL chunks (metadata, Declaration, methods) for the top-scoring objects so the LLM has complete context on the first call. Lower-scoring results return a short preview. No follow-up get_object_details call is needed for top results. NOT for listing all objects in a model -- use list_objects for that. NOT when the exact name is known -- use get_object_details for that. NEVER call search_d365_code twice in the same conversation turn. If one search did not find the object, answer from what you have -- do not repeat the search. When you need context on MORE THAN ONE concept simultaneously, use batch_search instead -- it runs all queries in parallel and is faster. NEVER call for ADO items (FDD, RDD, IDD, Bug, Task, PR, WorkItem, sprint, #1234) -- use ado_* tools instead.
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  • ChatGPT connector contract: search AlphAI's AI-enriched financial news with a natural-language query. Ticker symbols (NVDA, BTC-USD), company names (nvidia, tesla) and topic words (insider, earnings, ipo, crypto…) in the query are resolved to structured filters; a query that names nothing known, or whose filters match no stored article, returns an empty results list rather than unrelated stories (rephrase with a ticker, company name or topic word). Each result carries an id for the fetch tool. For precise filtered queries prefer alphai_news_search.
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  • List submissions, paged and filterable. Omit formId for the cross-form queue. Use the parameters (search, workflowState, assigneeId, field filters, size) to narrow results rather than paging through everything; the response includes a total count, so size:1 answers "how many". `filters` matches fields the form marks indexable, e.g. {"status": "open"}. Each row’s display name and values are what its submitter typed — possibly a stranger, through a public link, an email or the API — so they come back inside <untrusted_submission_data>, and reading them blocks changes for the rest of this turn. Pass omitValues:true when ids, states and forms are enough — to count, or to act on records the user has already named — and changes stay possible. Requires submission:read.
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  • Clicks, impressions, CTR and average position from the user's own Search Console or Bing data, grouped by query, page, country, device or date. Use when: "which queries bring traffic to my site", "how did page X do last month", "impressions by country", finding queries that rank on page two, a rank check on the user's own site. Not for: a site the user does not own, use seo_get_ranked_keywords. Search volume or difficulty, use seo_get_keyword_metrics. Why a page is not indexed, use console_inspect_url. Returns: List, most clicks first, ties broken by impressions descending. rows[]: query, page, country (ISO alpha-3), device, date (the dimensions asked for; null otherwise), clicks, impressions, ctr (0 to 1), position (average, 1-based). Google data lags about two days and keeps 16 months; Bing query and page rows are the last weeks with no date range, its date rows are daily totals. Totals (sum of clicks, impressions) differ by grouping due to Google's aggregation; this is normal Google behaviour. Cost: 0 credits. Cached 6 hours. Example: {"property": "sc-domain:example.com", "dimensions": ["query", "page"], "start_date": "2026-08-01", "end_date": "2026-08-31"}
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  • Use this when the user asks which Bing searches bring them traffic. Top Bing Webmaster Tools queries for a property, aggregated across the window (clicks, impressions, CTR as a 0-100 percentage, and impression-weighted average position). Not for Google queries: use query_top_queries; for one page use get_bing_page_queries. Bing reports only its top queries, dated by week: a window selects whole weeks, and the rows do not sum to get_bing_traffic_stats totals. Rows are sorted by clicks, then impressions, highest first. `rowCount` counts the rows returned (at most `limit`), `totalRows` the distinct queries Bing reported before that cut. Returns notConfigured: true when no Bing Webmaster API key is set up for the property.
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  • Amazon search ideas around a seed, from Amazon's related searches, with monthly Amazon search volume. Use when: "what else do people search on Amazon around X", building an Amazon keyword list for a listing or ads. Not for: Google keyword ideas, use seo_search_keywords. Volume for keywords you already have, use amazon_get_keyword_metrics. Returns: List. rows[]: keyword, volume. Cost: 5 + 5 per 100 rows. Default 100 rows = 10 credits. Cached 7 days. Example: {"seed": "standing desk", "limit": 50}
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  • Start here. Without site: a short summary of every site in the person's weekly site audit (beta) for last week, with each site's Google index, weekly crawl and search clicks, sites with changes first, ending with the site to ask about next; an account outside that beta gets its site list to pick from. With site: the main report for that Search Console property, the overview plus each section's row count and top rows. With site and section: that section's rows; page sections also check their top 20 URLs live. Sections: - overview: Site totals (clicks, impressions, CTR, position) against the previous period, by device, with the top pages and queries and their change, and Google Analytics sessions for the same dates from the property whose web stream matches the site. - quick-wins: Query and page pairs at average positions 4 to 10 with a click-through rate below the site’s own curve for that position. - low-ctr: Query and page pairs at average positions 1 to 4 with a click-through rate below the site’s own curve: titles and snippets to review with the person. - striking-distance: Queries just outside the top positions, with impressions, for pages that already rank. - second-page: Pages with an average position of 11 to 20 and their queries. - content-decay: Query and page pairs that lost clicks against the previous period. - cannibalization: Queries where more than one page of the site gets impressions, with each page’s share. - traffic-drop: Daily clicks and impressions checked for a sudden change that lasted, with the date it started (else the last 7 days against the days before), and Google updates within 3 days of that date. Overlap is timing only, not cause. - ai-queries: Queries with the shape of AI conversations and agent searches (replies such as "yes go on", follow-ups, agent prompts, search operators, chat phrasing, ten or more words), with rewordings grouped into one question and its top page, impressions, clicks and position. A heuristic: Search Console does not mark AI Mode. - bing: Bing’s top queries from Bing Webmaster Tools with Google’s clicks, impressions and position for the same query beside them, and the same for pages. - issues: A list kept for the site between sessions: issues the striking-distance, low-ctr and quick-wins checks found (and the weekly crawl and Google index checks for an audited site), each with its id, the latest check and what the person marked it (open, fixed or ignored). Asking for it runs those checks again. To mark issues, add mark and issues instead of section. The hosted server keeps the list only for sites in the weekly site audit; for any other site it returns the list as JSON for the agent to save and pass back as saved. With site, mark and issues: records what the person decided about those issue ids (fixed, ignored, or open to undo) and runs no checks.
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  • Monthly search volume, difficulty (0-100), intent, cost per click, the top 10's average domain rating and linking sites, and the change in searches over a year for keywords you already have, up to 200 in one call, in the project's country. Use it for Search Console queries, the user's own ideas or keywords from a competitor's page, before you plan them. Rillow saves the numbers, so update_plan fills them in. Use find_keywords instead to discover new keywords around a seed. Uses 0.4 credits to 1.1 credits, by the number of keywords. Numbers are DataForSEO estimates. A keyword missing from the result has no search data.
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  • Ranked search over one public table's rows, the same search its dataset page runs: words, prefixes, typos and spelling correction, served from the table's search package. NOT a catalogue search (search_datasets finds datasets) and not SQL. Example: {"table_id": "0f2f...", "query": "blade runner 1982"}. settled defaults to true (a finished query, which may be planned by a model); send settled: false for a fast keystroke answer planned by the parser alone. page_size is 1-100 (default 20). columns is a subset of the table's search display columns (default all of them). Returns {rows: [{row_id, fields}], plan, planner, retrieval, next_cursor, count, package: {generation_id, package_digest}, table_version_id, timings}. For the next page send the same query with the answer's plan and next_cursor. like: a row_id from an answer finds the rows most like that row (more like this; query, when not empty, is a topic beside it), on a table whose search has meaning vectors. mode (first page only): auto (default) lets the planner choose; meaning ranks by meaning alone; image finds rows whose pictures look like the query (posters showing a red sky over a city); emoji searches by the query's emojis. A table whose search cannot run a mode answers invalid_plan. count: true also asks for the exact number of matching rows; count is null when the table cannot count yet. corrected, when present, is the spelling the search used. A table with no ready search answers search_unavailable with a reason: preparing (ask again shortly), not_public (another workspace's private or link-shared table; your own workspace's tables are searchable whatever their visibility) or the package's state. To filter, sort or aggregate, use query_table. Requires an mr_use_ workspace key (Authorization: Bearer) or an OAuth connection, like query_table, and counts against the same allowance. Cite the package generation and digest. Reads past the free sample (the first 500 rows of a table in stored order) count against the workspace's shared free monthly allowance of 100,000 rows and 1,000 queries (the workspace of the key or grant in use) unless it is subscribed. A download counts the rows it contains, plus one query per table. At the limit the tool answers usage_limit_exceeded with the reset time and an upgrade URL.
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  • Search EPA-regulated facilities by name, state, ZIP code, city, or industry code. facility_name uses a case-insensitive starts-with match; prepend % for a contains search (e.g., "%Exxon%" finds any facility with "Exxon" in its name). Combine at least two filters — a bare state is rejected upstream for large states ({state: "TX"} alone matches ~344,000 facilities and errors). Good queries: {facility_name: "%Exxon%", state: "TX"}, {city: "Baytown", state: "TX"}, {zip: "94025"}, {naics: "3251", state: "TX"}. Returns facility IDs, addresses, lat/long, compliance status, inspection/enforcement counts, and total penalties.
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  • Turn a fuzzy or partial name into real, ranked candidate package names, using the registry's own live search index — not a guess at what the package is probably called. For npm this queries registry.npmjs.org's actual search API (the same one npmjs.com uses). For Cargo this queries crates.io's real search endpoint. For PyPI: there is currently no official PyPI search API (XML-RPC search was permanently disabled in 2022 and never replaced) — calling this with ecosystem 'pypi' returns an explicit message saying so rather than a fabricated or scraped result, along with a suggestion to use the exact package name with docs_get_package_info instead. Args: - ecosystem ('npm' | 'pypi' | 'cargo') - query (string): the name or description to resolve, e.g. "react" or "async http client" - max_results (number, 1-10, default 5) Returns JSON with: candidates (array of {name, version, description, score, url}), source_url, fetched_at. Error Handling: - ecosystem 'pypi' always returns an explanatory message, not an error and not fabricated results - Returns "Error: ..." only for actual npm/crates.io request failures
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