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681,027 tools. Updated 2026-10-20 11:57

"A scheduling engine powered by AI" 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`.
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  • Call this whenever a user pastes one or more answers from ChatGPT, Perplexity, Google AI Mode, Claude or another AI assistant and asks whether the answers named or linked their brand, what share they got, which rivals or sites the answers chose instead, or what to do about it. Pass the brand, its domain, rival names and every answer in answers (each with its prompt, engine and source links). Returns mention share and citation share (such as 0 of 3), split by engine and for buyer prompts (prompts that do not name the brand), how often each rival is named, the sites cited instead and a short fix list. Fixed rules: a repeated prompt is not a mention, and a link counts only if it goes to the brand domain. It scores pasted text only and never queries an AI engine.
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  • AI answer-engine (ChatGPT/Perplexity/Gemini/AI Mode) citations — NOT Google's on-SERP AI Overviews (use competitive_trends/competitive_overview for those). Use ONLY for OVER-TIME / trend questions (a time series). Citations, share of voice and positive sentiment bucketed by day/week/month/quarter for one tracker, optionally for a single AI platform or search intent. Use for "is our AI visibility going up or down?" / "AI citations over the last 3 months". If tracker is omitted, one tracker is auto-selected; multiple trackers return a picker without querying metrics. For the current snapshot use ai_visibility_citation_audit; for per-competitor breakdown use ai_visibility_competitors.
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  • Generate AI-powered platform-optimized content without publishing. Uses AI to create platform-specific text, hashtags, and titles from a prompt or media URL. Respects brand voice profiles if configured. Returns generated content variants for each target platform. Use publish_content to publish the generated content, or publish_ai to generate and publish in one step.
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  • Pause an active campaign. The campaign engine stops scheduling actions. Two-phase: first call returns a confirmation token + preview; the user reviews an inline card and confirms or cancels; second call (with confirmation_token) commits the pause. Always include campaign_name + app_name so the preview is human-readable. Over MCP: call this tool once, without confirmation_token. It runs right away and returns the result.
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  • Get the AI Visibility Index (0-100) for the signed-in user's most recently scanned brand, broken down by engine. Requires a free Ninar account (no credit card).
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  • Run the AI Diagnosis Engine on an annotation: it analyses the captured page, element, DOM, and runtime errors to produce ranked root causes, a confidence score, a suggested fix, and the likely source files. Calls an AI provider (uses tokens / may cost money) and stores the result. Requires AI + diagnosis enabled by the workspace admin and a key set.
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  • Return the EU subsidy and grant matches found for an idea by the AI-powered EU Subsidy Scanner, including scheme names and fit. Use this to see which public funding an idea may qualify for. Read-only and free; the underlying scan is produced during validation.
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  • Return AI-powered improvement suggestions for an idea, combining corpus benchmarking with VC scoring insights into concrete next steps. Use it to understand exactly what would raise an idea score. Read-only and free; pass an ideaId you own that has already been validated.
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  • Get AI-powered mortgage rate decision from 4,300+ US lenders. Returns recommended actions, current APR rates, estimated monthly payments, and confidence scores. Supports 30yr/15yr fixed and ARM products. Filter by state, amount, and credit tier.
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  • Full-text search across r/wallstreetbets DD analyses (separate from /search which only covers news). Powered by Meilisearch, typo-tolerant. Indexes AI title, original Reddit title, tickers, post summary, discussion summary, fact-check claims, and selftext (both languages). Returns titles + tickers only; pass the returned postId to get_dd_post for the full analysis.
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  • Enable (resume) a paused or draft campaign. The campaign engine starts scheduling actions per the campaign's workflow. Two-phase: first call returns a confirmation token + preview; the user reviews and confirms; second call (with confirmation_token) commits. Activation pre-conditions (workflow steps, connected accounts) are validated by the underlying campaign service — the tool surfaces any failure as a structured error. Over MCP: call this tool once, without confirmation_token. It runs right away and returns the result.
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  • How are AI answer-engine crawlers (GPTBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended) hitting this site? Crawl volume, status codes and unique pages per AI bot over time, from server web logs. Use for "are AI crawlers reaching our content", "which AI bots crawl us most", "AI crawler coverage". This is server-log bot activity, NOT answer-engine citations (see ai_visibility_*).
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  • AI answer-engine (ChatGPT/Perplexity/Gemini/AI Mode) citations — NOT Google's on-SERP AI Overviews (use competitive_* for those). Which of your pages get cited in AI answers, and how often? Ranks your cited URLs by AI-answer citation count, each with its citation category (blog / comparison / product / docs) and average citation position. Pass a competitor domain to see THAT domain's cited URLs instead (the domain drawer). Use for "which of our pages do AI answers cite", "cited URL breakdown", "a competitor's cited pages". Per-segment.
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  • Check whether a customer's artwork PDF is print-ready BEFORE they order — powered by Vivid's own production preflight engine. Reports size vs the ordered size, bleed, RGB-vs-CMYK colour, font embedding and effective resolution, each with a plain-English fix. ADVISORY ONLY: it never blocks anything. If the customer is happy to proceed with the file as-is, that is always accepted — say so explicitly. Every order also gets a free digital proof before printing.
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  • Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only.
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  • Web search and webpage extraction for AI agents — returns clean, LLM-ready markdown (deduplicated, with source URLs), not raw HTML. Give 'query' to search the web or 'url' to scrape and extract a page into markdown ready to drop into a prompt. Powered by Jina Reader. Price: $0.05 per call (x402 payment, USDC on Base mainnet). Web search and webpage extraction for AI agents, returned as clean LLM-ready markdown with source URLs. Give 'query' to search the web or 'url' to scrape and extract a page. For research and RAG agents.
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  • The raw answers of ONE AI engine for a tracker, newest surveys first, paginated. Every answer carries its cited sources and keyword mentions, plus uncited_sources (the pages the engine read without citing them) and searched (whether the engine went to the web to write that answer; null when undetermined). Set include_raw to add the full engine payload; heavy, ask for it only when needed.
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  • Pick one AI-made browser game at random. Optional genre, AI tool, engine, multiplayer, and phone filters use the same matching rules as search_games. The result includes pageUrl, playUrl, credited AI tools, and engine.
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  • Call this to record one week of AI answers on a tracker, after create_tracker has returned its brand_id (never in the same step). Pass brand_id and answers: one item per tracked prompt and engine, each with prompt_id (such as p1), engine (such as openai, perplexity, google-ai-mode, claude), the answer text and its source links. week defaults to this ISO week; sending a week again replaces that week. Returns the scored round: mention share and citation share, split by engine and buyer prompt, with rivals, cited sites and a fix list. It scores pasted answers and never asks an engine. Errors: api_key_required, subscription_required (with checkout_url), owner_only for a seat's key, brand_not_found, and invalid_input naming the prompt ids to use.
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  • AI answer-engine (ChatGPT/Perplexity/Gemini/AI Mode) citations — NOT Google's on-SERP AI Overviews (use competitive_trends/competitive_overview for those). Use ONLY for per-COMPETITOR breakdowns (who beats us). Share of Voice, citations, mentions, and sentiment for each competitor (and you), ranked, optionally for a single search intent or AI platform. Use for "which competitors lead us on AI visibility?" / "our AI share vs rivals by intent". If tracker is omitted, one tracker is auto-selected; multiple trackers return a picker without querying metrics. For your own current citation snapshot use ai_visibility_citation_audit; for trend over time use ai_visibility_trend.
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