"A search engine that searches multiple other search engines" matching MCP tools:

• serp_multiA

  Web Search & Data APIA

  Multi-engine web search (metasearch). One search query fanned out across 2-3 search engines (Brave, Yahoo, Yandex) with results deduplicated by URL and merged by reciprocal-rank fusion: consensus ranking across engines instead of one engine's bias, with per-engine rank attribution on every result. [$0.05/call]. Params — q: search query; engines: comma list of 2-3 engines: yahoo, yandex (web mode). news mode has a single live engine (bing), so multi-engine is web-only; num_results: max results per engine, cap 25; region: locale, e.g. us-en, uk-en, de-de; timelimit: restrict to past day/week/month/year (d|w|m|y); safesearch: string (on|moderate|off); mode: string (web|news) Example params: {'q': 'best vector database 2026', 'engines': 'yahoo,yandex'}

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• web_searchC

  texttostlB

  Web search across multiple engines (Google, Bing, DuckDuckGo, Brave). Costs $0.01 (USDC, Base). Returns JSON results: title, url, snippet, engine.

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• find_equivalentA

  Medical Terminologies MCPA

  Ranked unified search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, SNOMED CT, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets. `limit` caps candidates per terminology (default 5, max 10). Every candidate carries `match_score` (lexical similarity to the search term, 0-1) and `rank` (global position across all searched terminologies) — both computed by this server, since upstreams don't expose comparable relevance scores. Candidates from different terminologies whose titles are lexically identical are clustered in `groups` — a strong same-concept signal (absence of a group is NOT evidence of non-equivalence). Searches upstreams in English. For official pt-BR content, use the dedicated tools: `icd11_search`/`mesh_search` accept `language: "pt"`, and `cid10_search` is natively Portuguese.

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• list_saved_searchesA

  Moxlade — Upwork buyer intelligenceA

  Every search saved against this token, with whether it is currently matching. Call it to find the slug delete_saved_search needs, to check that a search you saved is actually running, or to see what you had before deciding whether to pay again. Takes no arguments — it lists what this token owns and cannot see anyone else's. Returns `searches`: one entry per saved search with its `saved` slug (the id every other tool takes), the `name` you gave it, `matching` — false when the plan has lapsed and the search is paused rather than deleted — the `prefilter` it runs, and `created_at`. Read-only, and readable on every plan state including expired. That is deliberate: someone deciding whether to pay has to be able to see what they had.

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• run_knockout_searchA

  GleanMark Trademark SearchA

  Run an examiner-style knockout search with scoring via the unified knockout engine — the same engine the GleanMark product uses. This is a PURE USPTO conflict search over 14M trademark records (exact, phonetic, trigram, component words, coordinated class expansion, doctrine of foreign equivalents, design codes) with mark-similarity and commercial-overlap scoring. Returns 4-tier risk-grouped results (very_high/high/medium/low) with confusion scores, plus a dead-mark "naming territory" sample. The top-line verdict is calibrated four-tier — CRITICAL CONFLICTS / ELEVATED RISK / MODERATE RISK / LOW RISK — with a one-line reason, so multi-name shortlists rank meaningfully. ALWAYS pass goods_description when the user has told you what they sell — the risk bands score goods/services relatedness, so an identical mark in a related-goods class reads VERY_HIGH only when the goods are supplied (class-only scoring understates it). It does NOT check domain availability and does NOT run a brand/web availability check — for that, use check_brand_availability instead. Most searches finish in under a minute; before calling, give the user a one-line heads-up that it may take up to a minute. Optional owner_name adds portfolio context — shows the applicant's existing marks in searched classes.

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• web_search_multilangA

  gapup-mcpC

  Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.

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• search_prop65A

  Proposition 65A

  Search the Proposition 65 list for chemicals whose name contains a fragment. Use this when you do not have an exact name or a CAS number, or to survey a family of related substances. Returns matching chemicals with their CAS numbers, toxicity endpoints, listing dates and delisted flags, capped at a limit with `truncated` set when there were more. It searches names only, so it will not find a chemical listed under a synonym you did not search for, and a result here is not a determination that a warning is required.

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• get_keyword_rank_historyA

  rankparse-mcpA

  Get the stored day-by-day ranking history for one keyword on one search engine over a date range. Reads RankParse snapshots synced on a schedule; it is not a live SERP check. History is inherently single-engine, so engine is required (use get_keyword_rankings first to compare across engines). Days with no synced data are simply absent from rows rather than filled with a zero or placeholder value -- do not treat a missing date as "not ranking". Bing and Yandex are opt-in and lower-frequency than Google; their responses may report availability as provider_limited, meaning that provider did not return enough data for that day rather than the keyword ranking nowhere.

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• generate_voiceA

  HermosoA

  RAW text-to-speech from the voice-model catalog: speak a script in a chosen voice and return the served MP3 URL. For a standalone voiceover / narration clip — NOT for adding audio to a video (render_ad and generate_video voice their own spots; change_voice re-voices a finished clip). engine picks the voice model (default 'seed-audio'; also 'eleven-v3', 'minimax-speech', 'kokoro'); voice is a preset name from that engine (see hermoso_capabilities → voice engines) — a name that engine does not have is REFUSED for free with its real list, and a few engines generate their own voice and take no preset at all (the reply says which voice actually spoke). Paid (a couple of credits by length; ≤900 characters).

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• create_campaignA

  xmagnetB

  Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.

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• search_codeA

  GithubA

  Search CODE across public GitHub repositories — find where a function/symbol/string is defined or used. PREFER OVER WEB SEARCH for "find code that does X", "which repos use <API>", "show me an example of <function>", "where is <symbol> defined". Supports GitHub code-search qualifiers right in the query: repo:owner/name, org:name, user:name, language:go, filename:Dockerfile, path:src, extension:ts, in:file. Returns matching files with repo, path, and URL. Note: indexes the default branch only, ignores very common terms, and is capped at ~10 searches/minute.

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• proximens_geo_list_categoriesA

  Proximens OracleA

  List the GEO principle taxonomy of the Proximens GEO Engine with a live count of high-confidence principles per category. INPUT: none. RETURNS: JSON with a categories array of {category, count, description} sorted by count, plus a reconciled total that matches get_stats.total_principles. Categories: technical, structured-data, ai-search, content, e-e-a-t, freshness, multimodal, user-signals, performance, query-intent, internal-linking, mobile, other. USE WHEN you want to discover which categories exist before narrowing a search_principles call with the category filter.

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• lorg_searchA

  lorg-mcp-serverA

  Search the Lorg knowledge archive. Use this to find existing contributions before submitting (to avoid duplicates) or to discover useful knowledge from other agents. Searches PUBLISHED contributions only; for the raw event/audit log use lorg_archive_query.

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• search_legislationA

  bill-commonsA

  Search FULL BILL TEXT -- not just known-bill-number lookup. `q` is matched against titles, descriptions, AND ingested document text via Postgres websearch_to_tsquery (supports "quoted phrases", OR, and -exclusion, same syntax as a search engine), with a fuzzy pg_trgm title-similarity fallback when the exact query has no hits. `q` can ALSO be a bill number ("HB 123", "H.B. 123", "hb123" all match) and that fast path is tried first. Optionally filter by jurisdiction (two-letter state code or name), chamber, and status. For a curated cross-state slice of a subject (e.g. "every AI bill in the country") rather than an ad-hoc keyword search, call list_topics first -- its membership rules also match on structured subject tags this full-text search does not see.

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• list_saved_searchesA

  Moxlade — Upwork buyer intelligenceA

  Every search saved against this token, with whether it is currently matching. Call it to find the slug delete_saved_search needs, to check that a search you saved is actually running, or to see what you had before deciding whether to pay again. Takes no arguments — it lists what this token owns and cannot see anyone else's. Returns `searches`: one entry per saved search with its `saved` slug (the id every other tool takes), the `name` you gave it, `matching` — false when the plan has lapsed and the search is paused rather than deleted — the `prefilter` it runs, and `created_at`. Read-only, and readable on every plan state including expired. That is deliberate: someone deciding whether to pay has to be able to see what they had.

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• search-designsA

  Canva

  Search docs, presentations, videos, whiteboards, sheets, and other designs in Canva, except for templates or brand templates. Use when you need to find specific designs by keywords rather than browsing folders. Use 'query' parameter to search by title or content. If 'query' is used, 'sortBy' must be set to 'relevance'. Filter by 'any' ownership unless specified. Sort by relevance unless specified. Use the continuation token to get the next page of results, when there are more results. CRITICAL REQUIREMENTS: 1. ALWAYS use the 'search-brand-templates' tool when the user is searching for templates or wants to use a template. 2.** 🚫 When a user says search a template, they ALWAYS mean brand-templates. Therefore NEVER call this tool, ALWAYS call the 'search-brand-templates' tool to search for the templates. ** 3.** 🚫 NEVER use this tool when the user expresses intent to “generate”, “create”, “autofill”, “search a template”, “start from a template”, “use my template”, or “pick a template for generation”. In all such cases, ALWAYS use search-brand-templates. ANY query involving: – “generate a presentation” – “generate a report” – “make a design using a template” – “generate from a template” – “produce a presentation from their template” - "search for available templates" MUST NOT use search-designs. This tool ONLY searches existing designs (docs, presentations, whiteboards, videos, etc.) that the user already owns or that are shared with them. It DOES NOT find templates and MUST NOT be used as a fallback for template selection. **

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• query_graphA

  Gonka Second Opinion (multi-model)A

  Search Gonka documentation. First searches the knowledge graph; if nothing found, automatically falls back to full-text search across all documentation files. This is the primary entry point for documentation questions — try this before read_doc or search_docs.

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• batch_searchA

  AnySearchA

  This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.

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  Destructive

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• get_catalogA

  ReefAPIA

  The FULL ReefAPI catalog — EVERY engine with its one-line title, grouped by category. This is the whole menu (≈ a few thousand tokens); SCAN IT AND PICK THE BEST ENGINE YOURSELF. You are an LLM, so you match the user's intent semantically — across ANY language, typo, or phrasing — far better than a keyword search can. Use this whenever search_engines didn't surface the right engine (or to be sure you didn't miss a better one). After you pick: get_engine_schema(engine) -> get_action_schema -> call_engine.

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• decodo_google_searchA

  DecodoA

  Google search results scraping via Decodo (formerly Smartproxy) — runs a Google search through rotating proxies and returns structured organic results (position, title, url, snippet) plus related searches when parsing succeeds. BYOK — _apiKey is your Decodo Web Scraping API "username:password" credentials. Example: decodo_google_search({ query: "best running shoes 2026", geo: "United States", _apiKey: "user:pass" })

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• search_jobsA

  jobstack-mcpA

  Search job postings across every configured source and merge the results into one flat Job shape. Fans out in parallel to USAJOBS, Adzuna, Jooble, The Muse, Reed, and the ats engine (Greenhouse/Lever/Ashby/Workable/SmartRecruiters), then merges + sorts by postedDate (newest first) and paginates the combined set. Each Job has id, source, title, company, location, remote, url, description, postedDate, salaryMin, salaryMax, salaryCurrency, department, and employmentType. The response envelope carries meta, the echoed query, sources_requested, an optional sources_errored map, total, count, limit, offset, and results. Use sources= to restrict to specific engines.

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