"deepseek" matching MCP tools:
- 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`.ConnectorNo auth
- Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.ConnectorNo auth
- TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks. Six frontier models (OpenAI, Anthropic, Gemini, xAI, DeepSeek, Perplexity) research each covered stock independently. Every model returns its own 0-100 score, rating (outperform / neutral / underperform), price target, and a weighted factor breakdown across financial performance, technical analysis, valuation, earnings call and corporate events. Use for: "what's the AI score for NVDA", "AI rating on my watchlist", "compare the AI scores of AAPL, MSFT and NVDA", "why do the models disagree on Tesla". Pass every symbol in one call — a multi-ticker call returns one compact row per ticker, which is what a watchlist or ranking question needs. A single ticker also returns every model's score with its factor breakdown plus the bull and bear key points. This is NOT the Smart Score (1-10, eight quantitative factors). It is a separate system, and the two routinely disagree by design. `ai_score` is the headline score and matches the AI Stock Analysis page; `consensus` holds the cross-model average, the high and low scoring models, and the split of rating labels. `upside_pct` is the model's price target against the current price. `as_of` is when the report was generated — reports regenerate on new earnings or a significant price move, so an older date means nothing material has changed since. Coverage is a subset of the stock universe and excludes ETFs. Symbols with no report at all come back under `not_covered`; symbols that are covered but lack a report from the requested `provider` come back separately under `no_report_from_provider`, each listing the models that did score them — so a missing provider is never reported as "this stock has no AI analysis". Args: tickers: Comma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT'), max 25. provider: Optional single provider to report on. Omit for the headline score that matches the website. detail: 'consensus' (default) or 'full' to add each model's written reasoning. Ignored on multi-ticker calls. Returns JSON: {stocks: [{ticker, company, ai_score, rating, headline_model, price, price_target, upside_pct, as_of, reflects, consensus: {models, avg_score, score_high, score_low, ratings_split, avg_price_target, avg_upside_pct, reports_dated}, providers: [...], key_points: [...]}], not_covered: [...], no_report_from_provider: [{ticker, covered_by}]}. `consensus.reports_dated` appears only when the models did not all run on the same date; `as_of` is always the headline report's own date.ConnectorNo auth
- Use this tool when the user asks whether ChatGPT, Claude, Perplexity, Gemini or another AI reads their landing page (ランディングページ, ホームページ, Webサイト), which AI crawlers fetched it, or how many visitors AI assistants sent (「AI に読まれている?」「ChatGPT から来た人は?」). Returns one page's AI access for 7d, 30d (default), or 90d; 180d, 365d, and all need Pro or MAX, as the other long periods do (AI reads were first recorded on 2026-10-25, so their days start there at the earliest; referrals cover the whole period, read from the long-term daily summaries for 180d, 365d, and all). - totals: ai (answer + crawl), answer (an AI opened the page because a person asked it something right then: ChatGPT-User, Claude-User, Perplexity-User…), crawl (AI crawlers: GPTBot, ClaudeBot, OAI-SearchBot, PerplexityBot…), and search (bingbot and Applebot, search crawlers that also feed Copilot and Apple's features, never counted in ai). - verification: the fetches by address check, each with the same four numbers. verified: from an address the agent's vendor publishes. noList: not checked (the vendor publishes no list, its list has no ranges of the address's family or wasn't refreshed for a week, or the fetch was recorded before the check); not checked doesn't mean fake. unverified: the vendor's current list covers that address family and the address isn't on it. unverified.ai ("self-named only", typically a scanner) is left out of totals and every list; unverified.search (bingbot, Applebot) stays counted, informational only. totals = verified + noList + unverified.search. - byVendor, byToken (each agent's User-Agent token: fetches counted, verified among them, unverified flagged, outside fetches except for search crawlers), byPath (host and path, no query), byDay. - referrals: visitors and pageviews whose utm_source or referrer names an AI assistant (ChatGPT, Perplexity, Claude, Gemini, Copilot, Grok, DeepSeek, Meta AI), with byAssistant. Assistants' apps send no referrer, so this is a floor. startDate and endDate are the days counted; coversPeriod false means a longer period could only be read for its last 90 days. visitorsMethod: unique_in_period (7d, 30d, 90d) or sum_of_daily_uniques (180d, 365d, all: each day's visitors added up, so someone who came on 3 days counts 3 times, as in the other long-term visitor counts). available false means page views couldn't be read right now, not zero. - liveDaysAvailable: today's and yesterday's AI reads are read live; false means they couldn't be read right now, not zero, so the totals leave those two days out. Agents are named by what the requests' User-Agents claimed; where the vendor publishes its addresses (OpenAI, Perplexity, Anthropic, Microsoft, Apple, DuckDuckGo, Google), each fetch is checked against them and the address is never stored. Only fetches of the live page are counted (not robots.txt or the sitemap). AI reads and referrals are on the workspace's days (fetchDayTimeZone, referralTimeZone; UTC when none is set); referrals for 180d, 365d, and all are on the days the daily summaries are cut in, which follow a zone change once it has finished rebuilding, as get_analytics_summary's long periods do. Right after the workspace's zone changes, AI reads stay in the earlier zone, as fetchDayTimeZone says, until an hourly run re-reads the page; fetchDayTimeZoneSince, when set, is the first day in fetchDayTimeZone, and earlier days are in the zone before. Neither is a page view or a visitor in get_analytics_summary.ConnectorOAuth
- LLM chat completion per call — no account, no API key, no token math. Three flat-priced tiers: fast $0.002 (DeepSeek v4 Flash), smart $0.02 (GPT-5.4 mini), reasoning $0.03 (DeepSeek v4 Pro). Send OpenAI-style messages, get the assistant reply with finish_reason and token usage. Input capped per tier (16k-32k chars); the 402 quotes the exact tier price up front. Model or source unavailable means a 503 and you pay nothing. USDC on Base.ConnectorNo auth
- Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit statusConnectorNo auth
Matching MCP Servers
- AlicenseAqualityDmaintenanceA general MCP server providing web search capabilities using DeepSeek's native online search API.150 npm33MIT
- AlicenseAqualityCmaintenanceEnables using DeepSeek models as a small, cheap supervised worker from any MCP-compatible client, providing fast flash and deep reasoning tools for bounded tasks.4254MIT
Matching MCP Connectors
Ultra-fast, native AWS edge-cached infrastructure delivering high-quality synthetic logic reasoning datasets for multi-turn AI model training.
Skavio LLC provides 18 hosted MCP tools for native Circle USDC payments via x402 v2 on Base and Solana: transaction verification, recipient/amount matching, blockchain evidence PDFs, DNS/TLS audits and agent utilities. Discovery and status tools are free; paid MCP calls require explicit buyer wallet authorization. The separate standalone Base-only HTTP APIs include USDC Verify (0.02), Match (0.03), Evidence PDF (0.05), and four DeepSeek-only web operations: Fetch (0.01), Search (0.02), Extract (0.02), Extract + Analyze (0.03 USDC). Those HTTP routes are not additional MCP tools. Evidence PDFs are not tax invoices. Docs: https://getskavio.com/x402/docs/ ; seven-route discovery: https://getskavio.com/.well-known/x402 ; support: support@getskavio.com ; privacy: https://getskavio.com/privacy/ ; terms: https://getskavio.com/terms/
- Calculate multi-provider LLM API inference token costs, prompt caching economics (up to 95% discount), batch API savings (50%), and cross-model cost disparity multipliers across frontier and high-efficiency models (Anthropic Claude, OpenAI GPT, Google Gemini, DeepSeek). Behavior: Deterministic, idempotent calculation with zero external side effects. Models official public provider pricing cards per million input/output tokens. Incorporates prompt cache hit pricing reductions and asynchronous batch API discounts. Evaluates real-time pack age and freshness status (FRESH < 14 days, AGING 14-30 days, STALE > 30 days). Returns comprehensive model cost matrix, cheapest and most expensive model arbitrage analysis, cache savings, and monthly cost projections. Usage Guidelines: Use when budgeting AI agent inference costs, evaluating LLM providers, or deciding whether to implement prompt caching or batch inference. Do not use for cloud network egress; use cloud_egress_finops instead.ConnectorNo auth
- 按五大核心指标 + 语义角色分权,计算 AI 搜索可见度并输出可自验的测量报告。 输入 samples 为 JSON 字符串: {"brand":"合尘猫","domain":"savantcat.cn","baseline_negative":3, "fact_points":["服务范围","交付周期","定价方式"], "samples":[{"question":"Q1","platform":"DeepSeek","run":1,"answer":"原文回答……", "role":"independent|joint|citation_only|none", "sentiment":"positive|neutral|negative", "facts":"accurate|partial|wrong|unverifiable", "facts_found":["服务范围"],"as_of":"2026-09-19"}]} 口径:每题建议多轮(同一问题重复 7-8 次);role 分级对应语义角色权重; 返回五大指标、语义角色加权分、逐题明细与「原文摘录」(便于客户自验)。 verify_citations=True 时会实测 AI 回答里引用的 URL 是否真的可访问(幻觉守卫); extract_claims=True 时会把回答中的数字声明单独列出并标记为未核验。ConnectorNo auth
- Runs a prompt against up to 5 REAL, directly-verified vendor models — you_com (You.com Answer API), deepseek, grok (xAI), meta_ai (Meta's Model API) and minimax — each called through that vendor's own public API. Unlike explore_ai_prompt's persona labels, every result here reports the real vendor model id in `modelName`. A model whose API key is not configured on this deployment answers `status: "unavailable"` with no call made — a deployment fact, not an error; `status: "error"` means the vendor is configured but the call failed. `webSearch` is best-effort per vendor: You.com is always grounded, grok honours it, and deepseek, meta_ai and minimax always answer `webSearch: false`. meta_ai is a US-developers-only public preview, so an ineligible account reports an error naming that. Billed on the operator's own vendor keys: no plan gate and no MisarSEO credits are spent.ConnectorNo auth
- Send a prompt or a conversation to a language model (Claude, GPT, Gemini, Grok, DeepSeek, Kimi) and get the reply. Defaults to anthropic/claude-sonnet-5-5. Pass system for instructions, messages for multi-turn or vision (image parts), json for JSON mode. Returns the reply text plus finish_reason and token usage. Synchronous: no task to poll. Bills per token on the account balance; dry_run_cost does not apply to LLMs (cost depends on the actual tokens) — the charge shows up in list_tasks with category 'llm'. This is for running ANOTHER model on the user's behalf (a second opinion, a specific model they asked for, bulk/structured extraction) — not for answering something you can answer yourself.ConnectorDestructiveNo auth
- Shortcut for get_region_signal with region "china": the current China & East-Asia AI signal (Qwen, DeepSeek, Zhipu, Moonshot, ModelScope, and more) that Western feeds often miss — ranked by momentum. Prefer get_region_signal if you may query more than one region.ConnectorNo auth
- Static editorial recommendation -- NOT a live benchmark or leaderboard -- for which of six AI models (GPT-5, Gemini, Grok 4, Claude, DeepSeek, Kimi) to use for a given kind of task, ported from GO AI's own daily side-by-side-use judgement, current as of August 2026. Pick a task and get the recommended model plus the reasoning and a second-opinion backup. The optional `priority` can bias the pick toward cost (routes to DeepSeek) or freshness (routes to Grok) instead of the default quality pick -- but only for tasks where that tradeoff is actually offered; otherwise the quality default is returned unchanged. This reflects one team's opinion, not measured accuracy or pricing data.ConnectorNo auth
- Search what people said outside earnings calls, in two kinds of source: - Interviews: podcasts, TV interviews and conference talks by company executives, investors, fund managers, analysts, economists, journalists and other guests. - Central-bank documents: speeches, testimony, policy statements, minutes, press-conference transcripts, projections and reports from the Federal Reserve Board and regional Reserve Banks, the European Central Bank and euro-area national central banks, the Bank of England, the Bank of Japan, the Reserve Bank of India, the People's Bank of China, the Bank of Korea, the Hong Kong Monetary Authority, Taiwan's central bank and others. Documents in other languages yield English paraphrases. Use it for questions about what a named person or institution said: "what has Waller said about rate cuts", "what did the ECB say about inflation at its last meeting", "what did Jensen Huang say about export controls". Claims cover listed and unlisted companies (OpenAI, DeepSeek, MiniMax), institutions (the Federal Reserve, the European Central Bank, regulators), and whole markets and industries, not only the speaker's own employer. How to search: put one to three key words in query, and put the speaker in person, the publisher in institution and the kind of document in appearance_type. A returned row must contain most of the query words, so a long question returns fewer rows than its key words alone. Retry with the speaker's own vocabulary or a synonym before concluding there is no coverage. No serving-time model interprets or expands the query: retrieval is lexical, not semantic. It tries strict full-text matching first, loose any-term full-text matching only when strict matching returns no candidates, then trigram matching only when both full-text stages are empty. Canonical search_target values: - "search" (default): blended structured claims and broad passage cards. - "claims": normalized decision-relevant claims only. - "passages": broad substantive paraphrase cards only. - "pressure_points": stored refusal and declined-to-confirm claim rows. - "appearances": video-level catalog rows. - "coverage": company-level structured-corpus inventory. "all" maps to "search", "videos" to "appearances", and "companies" to "coverage" for older clients; "search" is the primary blended target, not a legacy alias. Result contracts: - search/claims/passages/pressure_points return result_kind, canonical_paraphrase, speaker, primary ticker, topic labels, video/channel/publication metadata, lexical relevance, and source_url (a timestamped YouTube deep link, the episode page for a podcast, the document page for an official document, or the PDF page of a press-conference transcript). detail=true adds normalized claim and attribution fields plus institution, document_kind and event_date (null for interviews), but never raw transcript text or evidence spans. - appearances returns video metadata, primary ticker, attribution metadata, matching claim count, URL, and relevance score. detail has no effect. - coverage returns company-level appearance, publication-date-span, channel, claim, and low-confidence-attribution counts. It is structured coverage, not a per-person acquired-video/hour inventory; query, sort, and detail have no effect. Filter and ordering semantics: - company is comma-separated tickers and company names, mixed freely. A stored ticker matches the appearance's company or a section subject with that ticker; any other value is read as the name of a company, institution, or organisation a podcast section is about, matched after dropping punctuation, a leading "The", and corporate suffixes. Every ticker attached to a matched name joins the ticker filter, so "Alphabet" also reaches GOOGL rows. The result records company_tickers, company_names, company_matched, and company_unmatched (a value that matched nothing, with up to five possible matches); an unmatched value returns no rows, not an error. company_filter is the old name of company and behaves the same; passing both is an error. The appearances and coverage targets also match every value, upper-cased, against the entity references of claims (products, rivals, partners such as "CUDA"); a value found only there is listed under company_references. - person is a case-insensitive substring over the finding speaker and/or appearance speaker metadata. appearance_type applies to every target: podcast, tv_interview, conference and other for interviews; speech, testimony, minutes, statement, press_conference and hearing for official documents. country ("US") restricts every target to official documents of that country; institution ("Federal Reserve") is described with show and channel below. - claim_family restricts structured claims. With blended search it suppresses passage cards; pairing it with search_target="passages" is an error. For appearances/coverage it requires videos with matching structured claims. - date_from/date_to are inclusive YYYY-MM-DD bounds on the video's stored publication/upload date, which can differ from the recording or event date. - show, channel, and institution are case-insensitive substrings of a podcast show name, a YouTube channel name, and a publishing institution; each returns only rows of its own kind, and when more than one is given rows matching any of them are returned. - relevance orders lexical score first, then publication date; newest orders publication date first, then lexical score. Coverage has fixed inventory ordering by latest covered publication date. - limit is one page of 1-50 rows and offset skips that many ranked rows before the page, so limit=15 with offset=15 returns rows 16-30 of the same ranking. Every result carries has_more, plus next_offset when more rows follow; call again with that offset to read the next page. With an empty query, each target browses its most recent rows (search and claims show claims only). Evidence safety: canonical_paraphrase is a normalized paraphrase, not a transcript quotation. Cite source_url (timestamped for YouTube, page-anchored for PDF transcripts) with the returned speaker, publication date, and video or document metadata. FOMC minutes describe the meeting of event_date and are published three weeks later; the Committee, not one person, is their speaker. Do not put canonical_paraphrase in quotation marks unless you independently verify the wording at the source link. The tool never returns raw transcript text. Detail-field safety: internal audit flags are not published; judge a claim from value, assertion_status, and the timestamped source link. company_attribution_confidence and company_attribution_source concern only the video-to-company/ticker assignment; inferred attribution is not independent confirmation.ConnectorAPI key
- Returns YARA/Sigma detection rules for an ICS malware family or threat actor. Rules sourced from public corpus (Florian Roth signature-base, CISA advisories) are marked validated:true and safe for staging. DeepSeek-synthesised rules carry validated:false and require lab testing. Designed for automated threat hunting pipelines that commit rules to SIEMs and EDRs. Price: $0.05 USDC per call via x402 on Base mainnet. An unpaid call returns an x402 PaymentRequired challenge; retry the identical call with the signed payload in _meta["x402/payment"].ConnectorNo auth
- Read the curated crosswalk from CAC filer legal names to the US Entity List and Treasury NS-CMIC list. Search by Chinese or English entity-name substring; tier defaults to A. Tier A has published evidence for a legal-entity match. Tier B is a possible match, not confirmed, and is outside headline counts. Each entry gives the list name, start date, evidence type and text, and matching CAC record IDs. The Entity List imposes export-licence requirements for specified items subject to the EAR; the NS-CMIC investment ban covers specified US-person purchases or sales of publicly traded securities. These are different restrictions, not generic sanctions. The separate US actions timeline includes the DHS DeepSeek order with Federal Register links. MORVS does not determine whether any CAC DeepSeek filer is the company named by DHS. Not being on either US list never means an entity is cleared.ConnectorNo auth
- Read the curated crosswalk from CAC filer legal names to the US Entity List and Treasury NS-CMIC list. Search by Chinese or English entity-name substring; tier defaults to A. Tier A has published evidence for a legal-entity match. Tier B is a possible match, not confirmed, and is outside headline counts. Each entry gives the list name, start date, evidence type and text, and matching CAC record IDs. The Entity List imposes export-licence requirements for specified items subject to the EAR; the NS-CMIC investment ban covers specified US-person purchases or sales of publicly traded securities. These are different restrictions, not generic sanctions. The separate US actions timeline includes the DHS DeepSeek order with Federal Register links. MORVS does not determine whether any CAC DeepSeek filer is the company named by DHS. Not being on either US list never means an entity is cleared.ConnectorNo auth
- AI Visibility 360 | the caller's OWN brand-visibility report across the AI assistants (ChatGPT, Claude, Gemini, Perplexity, optionally Grok/DeepSeek/Mistral), read deterministically from stored runs server-side (the exact numbers the user sees in the app | nothing re-derived, NO LLM runs on this read and no run is started). In one sentence: which brands ChatGPT, Claude, Gemini and Perplexity recommend when someone asks about your category. Call it when a user asks "how visible is my brand in ChatGPT", "do assistants recommend us or a competitor", "which sources do the assistants cite", "what should we do to show up more", "did the AI visibility work turn into real traffic". Sections: overview (visibility score with delta and rank, the brand-vs-competitor leaderboard with visibility / share of voice / sentiment / average position, the per-provider score matrix and the concrete models that answered), prompts (per-prompt brand score vs the strongest competitor plus per-question-category rollups), sources (citation share of the brand's own domains, the cited-domain leaderboard, which providers expose citations at all), actions (the deterministic to-do queue: earned = pages to get featured on, owned = pages to build, each with impact and status), answers (the newest stored assistant answers with detected brand mentions and cited domains, text truncated honestly), impact (GA4 sessions referred by AI assistants for the property explicitly linked to this brand; an unlinked brand gets the honest empty state and the reason, never another property's numbers). EVERY number here counts only questions that do NOT name your own brand: a question naming the brand has already handed the assistant the answer. That holds for visibility, share of voice, rank and the per-assistant matrix AND for sources, gaps, pages, assistant searches, domain movers, perception and action effects. The questions that do name it are still measured, in `rankings_branded`, and the `population` block (present on every section set) carries both counts | never add the two together. The two evidence views keep every row instead: the prompt table (flag `names_you`) and `answers` (flag `question_names_your_brand`). A metric the window cannot support is null or absent (an honest dash), never a zero. Reads ONLY brands owned by the calling account; runs, prompt edits and settings are deliberately not exposed here. Recipe: pull the sections you need and interpret them yourself, citing the numbers. For a custom deliverable, write your derived table with create_dataset + write_rows and chart it with create_chart_from_spec. Requires the caller's own autario account (API key or OAuth) with an AI Visibility brand set up | see get_app_context("ai-visibility").ConnectorNo auth
- FREE. Resolves a partial name into exact model IDs and dataset names you can pass to the other tools; returns two string arrays (models, datasets), capped at 25 each. Use this FIRST whenever you have a fuzzy name like 'deepseek' or 'gas' and need the canonical identifier. Do NOT use it to retrieve values or search inside records — it matches names only; use anansi_price_current for prices or anansi_data_search (paid) to search every field of every dataset. Input: q is a case-insensitive substring.ConnectorNo auth
- 审计一个网站/页面能否被中文 AI 搜索(豆包、DeepSeek、文心、Kimi 等)抓取、解析与引用。 一次抓取完成六层检查:可抓取(含中国爬虫矩阵与边缘层拦截实测)、可解析、可引用、 实体一致、分发、可信可自验;返回 0-100 分与 A-F 等级、逐项证据、优先修复清单。 用途场景:客户站点体检、上线前自检;传 compare_with 可做竞品对比(逐层与逐项差异)。 注意:只做确定性检查,不调用大模型;评分口径见 scoring_version。ConnectorNo auth
- Assesses how much a general-purpose AI agent lowers the skill/time barrier for an attacker to reach a vendor's OT-adjacent footprint. Grounded in a real-world precedent (Dragos, May 2026), not frontier-model governance thresholds. Distinct from /ot/ai-exposure, which covers a vendor's OWN embedded AI copilot. DeepSeek-synthesised, ICD-203 estimative language. Price: $0.20 USDC per call via x402 on Base mainnet. An unpaid call returns an x402 PaymentRequired challenge; retry the identical call with the signed payload in _meta["x402/payment"].ConnectorNo auth
- Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning depth. Useful for o1/o3/DeepSeek-R1 evaluation.ConnectorNo auth