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741,773 tools. Updated 2026-11-04 09:00

"SearXNG - Open Source Metasearch Engine" matching MCP tools:

  • Pro-tier. Fetch two web pages (your URL and a competitor's) and audit both against the Proximens GEO Engine principles using the same audit engine as audit_url, then compute the delta. INPUT: self_url and competitor_url (both required, http/https). RETURNS: JSON with a 0-100 score per URL (same scoring as audit_url), the principles each page satisfies, the principles each page VIOLATES that the other satisfies (delta_principles), and strategic insights on where to close the gap. USE WHEN you want a competitive GEO gap analysis between your page and a rival's.
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  • 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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  • Evaluate up to 24 templates/views in ONE round trip from the same session dataset build — a dashboard's dozen nuggets paint together and always agree. Each requests[] item is an evaluate body ({view|source|templateid, periodStart?, periodEnd, ...}); each results[] slot is exactly what /report/session/evaluate would return for it, errors inline per slot. Requires an open session (open_report_session first); for a single view use evaluate_report_session, or evaluate_report for a one-off outside any session.
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  • Save a template’s draft. Both kinds take structured `content` — see create_document_template for the node format; notifications use its EMAIL profile (no image, no pageBreak) — or, for a template still on the legacy HTML engine (check get_template_draft’s `engine`), a `body`; a structured draft refuses body-only writes. Notifications also take subject and recipients; documents also take `settings`. Editing a live template’s draft does not change what sends until publish_template. A document on the SVG facsimile engine (engine "SVG") is edited with edit_svg_template, never here. Requires notification:write or document:write.
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  • Return the list of ARCO variables available for a dataset. Each row's type (index 2) is either "zarr" or "reference" (kerchunk). A "reference" variable is often listed multiple times, once per kerchunk reference variant (plain, "-remote-https", "-remote-osdf", etc.) — always pick the "-osdf" variant's URL; the others' chunk targets can be internal paths that only resolve on NCAR's network. Open it with xr.open_dataset(url, engine="kerchunk", storage_options={"remote_protocol": "https", "lazy": True}) — that's better behaved than hand-building an fsspec reference filesystem. A "zarr" row needs no variant picking — its URL already routes through the OSDF director (rewritten automatically); open it directly with xr.open_dataset(url, engine="zarr"). Results are capped at 300 rows (look for "truncated": true); narrow with search_arco_variables if you hit the cap. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002
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  • Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six. It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of <collection>" — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.
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  • Hotel and travel feed validation, normalization and requirement comparison.

  • Read, write, and conversationally review open-source flashcards through split read/write MCP tools.

  • Engine version, API contract number, and health. Free (not quota-counted). Call once at the start of a session to confirm the engine is reachable and which contract it serves.
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  • Run a historical backtest against the engine. Quota-counted and compute-bound. Validate the strategy first (validate_strategy is far cheaper). On a 504 compute timeout, do NOT retry the same request — reduce the date range, use a coarser frequency, or simplify the strategy. On 429/503, wait for the advertised Retry-After before retrying. Args: data_source: Either inline OHLCV ({"ohlcv": {dates, open, high, low, close, volume?}} as parallel arrays, ISO-8601 dates) or a server-side fetch ({"symbol", "start", "end", "frequency"} — requires a paid plan). strategy: Strategy document (indicators[] + condition_tree). Mutually exclusive with signals. signals: Precomputed signal series ({"dates": [...], "values": [-1|0|1, ...]}). Mutually exclusive with strategy. execution: Execution/cost/risk/sizing settings. Use values from get_catalog('execution-modes'/'stop-types'/'sizing-methods'); omit for engine defaults. benchmark: Optional benchmark data source (same shape as data_source) — when given, the result also carries benchmark-relative metrics (beta, alpha, information ratio, tracking error, up/down capture) and bar-alignment info. data_inputs: Optional custom time-series the strategy references (name -> {dates, values}). response_detail: 'summary' (default — headline metrics, smallest), 'stats' (every metric), 'full' (plus trades and series downsampled to a fixed, server-controlled number of points). include: Optional add-on blocks at any detail level: 'trades', 'equity_curve', 'monthly_returns', 'yearly_returns', 'signal_diagnostics' (which per-bar entry/exit conditions fired, as capped fire-date lists — {"available": false, ...} if the run has none, e.g. precomputed signals). trades_limit: Max trades returned when trades are included. Returns: The shaped result at the requested detail (including ``benchmark_relative``/``alignment`` when a benchmark was given); an oversized result is thinned and marked ``truncated_by_mcp``. If the engine rejects the request as invalid (400/422), returns {"accepted": false, "error": ...} so you can fix the named field(s) and retry. Capacity, timeout, and permission failures (e.g. 429/503/504/401/403) raise a tool error carrying explicit recovery guidance.
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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
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  • List the ProductClank content spaces you can draft into — spaces the user owns, delegates for, or manages that have their content engine turned on. Returns { space_id, name }. Call this first to resolve the space_id for write_content_candidates, and confirm the target space with the user.
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  • Use this when the user needs explicit corpus scope, result limits, or a rich normalized list rather than the standard citable search/fetch flow. It is read-only: the default uses site-controlled actionable U.S. trials and savings, while only an explicit open-source scope reads public GitHub. Use public-benefits only for eligibility-based services. Do not use it for purchases, account access, or guaranteed eligibility.
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  • Searches currently open French public procurement opportunities and returns normalized BOAMP tender data ready for automated processing. Use when: You need currently open French public procurement opportunities from BOAMP. You want to search open tenders by keywords, department, buyer or minimum deadline. Avoid when: You need contracts already awarded to a specific company; use company/fr/public-contracts. You need private procurement opportunities or a guarantee that every French public procurement notice is covered. Limitations: Coverage is limited to the BOAMP public dataset and the notices available through that source. At least one of query, department or buyer is required; tender deadlines and notice details can change at the source. Price: 0.010 USD per call via x402. Paid-call behavior: source records are not modified, but a successful invocation can consume the advertised x402 payment; the payment-required challenge is free, and repeating the tool after a completed paid invocation may require a new payment. Accepted x402 networks: eip155:8453, xrpl:0.
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  • Lists classified events the rules engine could not confidently categorise (needs_review = true, not yet synced to the ledger), newest first. The pipeline is PAUSED for each of these until someone approves a category — they are missing from P&L reports and the ledger. Every item carries what an approver needs to decide without further queries: the source system, event type, amount in cents, currency, direction (inbound = money in, outbound = money out), when it occurred, the counterparty, the category the classifier suggested, and flagged_reason explaining why it was flagged. Resolve an item by calling approve_classification with its classified_event_id and the correct category. Paginate via next_cursor; total_count is the full queue size.
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  • Volume, difficulty, intent, CPC and 12-month trend for keywords you already have, with AI-engine prompt volume on request. Use when: the user supplies keywords, or you need numbers for a list from elsewhere. ai_volume true when the question is how often AI engines see the keyword. Not for: discovering keywords, use seo_search_keywords (it already includes these metrics, do not call this after it). AI-engine prompts, use aeo_search_prompts. Checking rankings, use seo_get_position. Returns: List. rows[]: keyword, volume, kd, intent, cpc, competition, bid_low, bid_high (Google Ads top-of-page bid range, USD), trend[12], ai_volume, ai_trend[12]. One row per input keyword in the order given. volume/kd null = provider has no data; 0 = measured zero. With source google_ads, kd and intent are always null and keywords over 80 characters come back all null. ai_volume and ai_trend are null unless ai_volume is true. Cost: 5 + 5 per 100 keywords with source labs; 24 flat with source google_ads; ai_volume adds 4 + 4 per 100 keywords. Default 100 keywords = 10 credits. Cached 7 days. Example: {"keywords": ["crm for startups", "hubspot alternative"], "ai_volume": true}
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  • The Search Console and Bing Webmaster properties the workspace's connected accounts can read. The first console call; every other console tool takes a property from here. Use when: "what sites are in my Search Console", before any console_get_* call, or to check which engine is connected. Not for: third-party sites the user does not own, use seo_get_domain_overview. Live rankings, use seo_get_serp. Returns: List. rows[]: engine (google | bing), property (the id to pass on: sc-domain:example.com, https://example.com/ or a Bing site URL), permission (Google: siteOwner, siteFullUser, siteRestrictedUser, siteUnverifiedUser; Bing: null), verified. NotConnected when no account of that engine is connected; its connect_url is where the user connects one. Cost: 0 credits. The user's own data is free; 60 console calls per minute per workspace. Cached 1 hour. Example: {"engine": "all"}
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  • Report an answer you believe is wrong or missing, so Fuentio checks it against the official source. Use it when a result contradicts an official document you hold. Don't use it to ask questions: call the lookup tools. Inputs: subject (entity ID, record ID such as 'FR-SIREN-441639465', or tool name), category (wrong_data, wrong_link, missing_data, wrong_status, other), message. Returns: issue_id, status 'open'. Limits: free; API key required; 20 reports per key per day.
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  • Video Director render: action=start (projectId, expectedRevision, idempotencyKey, kind=frame|contact|preview|export, startFrame, endFrame) returns a job to poll with video_jobs. An export always covers the whole project; to test a range, render kind=preview with startFrame/endFrame. A job only succeeds when the video stream itself holds every planned frame, and result.duration is probed from the file. cancel (jobId); retry (projectId, expectedRevision, idempotencyKey, jobId). Renders happen on the private engine; the MP4 is downloaded from AMZ Vault, never published anywhere. Video Director is in BETA (2026-10-18): open to every AMZ Vault account; free video accounts get 3 badged 720p exports, trials 2 clean exports a day, subscribers unlimited. Call video_guide first for the catalog, the exact input schema of each action and this account's tier notes.
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  • Publish ONE open A2A source-review task (SendMessage), after the user approved the exact request returned by cairn_a2a_preview_task. Public and cannot be removed. Each task needs its own approval, so there is no ongoing scope. A low daily cap applies per identity. Reuse the same request (and its operation_id) for an uncertain retry.
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  • Finds deployable apps in NetActuate's marketplace (netactuate.com/marketplace): streaming servers, transcoders, WebRTC, CDN and media software that deploys on NetActuate's edge, open source and from partners such as Wowza, Broadpeak and NETINT. Filter by any mix of words, vendor, category, licensing and freeDemo; with no filter it lists them all, featured first, `limit` at a time. A vendor or category it does not know is explained in a note naming the valid ones. Each result gives the slug, name, vendor (null for open source), tagline, licensing (free, byol, trial), vpu when it needs NETINT VPU hardware, freeDemo with its terms, sizing (minimum and recommended vCPU, RAM and disk) and its URL; count is the full number of matches. Use for 'what can I deploy on NetActuate', 'is there an SRT gateway', 'which apps can I try for free' or 'what does NetActuate offer from Wowza'. One listing in full: get_marketplace_app; the vendors: list_marketplace_partners. Listings do not say where they run; for an app and a place together, use search_netactuate ('Wowza Africa').
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  • Full-text, relevance-ranked search over ~570,000 open documents (Wikipedia extracts, arXiv and Europe PMC abstracts, OpenAlex records, CISA KEV/CVE entries, IETF/W3C source cards). Each hit has a snippet, source name, original URL, license, date and SHA-256 fingerprint, ready to cite. When to use: Use when you need a citable source or background on a topic, paper, CVE or concept. Use plain keywords or a short phrase (exact phrases rank first). Then call read_document for the full text. Do not use it for HTTP RFC wording (use search_clauses). Notes: No account or token needed. Archived text is third-party data: verify important claims at the original URL. Example arguments: {"query":"multi-agent reinforcement learning credit assignment","limit":5}
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  • Find source-reported open comment periods, soonest indexed deadline first. Covers proposed rules, notices, AND final/interim rules that accept comments (dozens of RULE-type documents have open periods at any time). Queries for comment close date >= today, scans up to 500 matching documents oldest-published first (where the soonest deadlines live), sorts by close date, and returns the first `limit`. Source scope: total_open is the API's matching metadata count; scanned is how many this call examined. When total_open exceeds scanned, narrow with agencies/term for complete indexed coverage. A very recently published document with an unusually short comment window can fall outside the scan in that oversubscribed case. complete means the matching indexed records were scanned, not that every currently open opportunity or controlling deadline was verified. The API's comments_close_on metadata can disagree with DATES after an extension or reopening. Even a zero result needs docket/subject search_documents and get_document DATES/linked publication checks before ruling out comments. Default: searches all agencies. Pass agency slugs to narrow scope, e.g. ['defense-acquisition-regulations-system'] for DFARS or ['general-services-administration'] for GSA and GSAR. Parameters: - far_council: True for open FAR proposed rules and FAR information collections (documents filed jointly by DoD, GSA and NASA; there is no single FAR agency slug and the OFPP tag only appears from about mid-2025). Cannot be combined with agencies. - limit: max documents returned after sorting (default 50, max 100). Unfiltered dumps across all agencies can approach 200KB; several broad agencies together (e.g. DoD + GSA) can pass 100KB, so lower limit for those.
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