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306,652 tools. Last updated 2026-07-25 18:37

"Information on Vision Pro" matching MCP tools:

  • Create a new always-on database owned by the Pro account. Beyond the included allotment this costs extra per month — the tool then returns confirmation_required with the exact price; relay it to the user and only retry with accept_overage_usd after their explicit approval.
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  • Upload an image to a brand by URL. The pipeline downloads it, runs the vision tagger (classifies type, detects product name, flags is_primary_product), stores it in the brand-assets bucket, and inserts a brand_assets row. Paid (vision tag credit). If vision tagging fails, the asset is still saved with type=general and can be retried via retag_brand_asset.
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  • Register a new agent on Human Pages. Returns an API key (hp_...) that you MUST save — it cannot be retrieved later. The agent is auto-activated on PRO tier (free during launch): 15 job offers/day, 50 profile views/day. Use the API key as agent_key in create_job_offer, get_human_profile, and other authenticated tools. Typical first step before hiring.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • Check if email address uses a known disposable/temporary provider (Guerrilla Mail, Temp Mail, Mailinator, etc.). Use for input validation to detect throwaway signups; for domain reputation use threat_intel. Companion email-investigation tools: email_mx (deliverability + MX trust), domain_report on the email's domain (full recon), threat_intel (malware-distribution signal on the domain). Free: 30/hr, Pro: 500/hr. Returns {disposable, domain, provider}.
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  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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  • Find personalized puzzle books by first name from a 100,000+ title Shopify catalog.

  • ship-on-friday MCP — wraps StupidAPIs (requires X-API-Key)

  • Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.
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  • Use this when you need to trace features from a reference photo into waypoints. Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.
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  • Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources. Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers. Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant. NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search.
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  • Get full details for a single Civitai model by id — description, type, creator, tags, download/rating stats, and every version with its base model, trigger words and downloadable files. Try id 4201 ("Realistic Vision V6.0 B1", a SFW checkpoint). Defaults Safe-For-Work; nsfw flag is surfaced. Keyless.
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  • Initiate the upgrade to PRO ($15/mo flat). Returns a Stripe checkout_url that a human opens once to add a card — agents cannot complete the payment themselves. Moves the account from sandbox (sk_test_) to live sends. Returns status 'updated' if already on the plan.
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  • Start a monthly plan. Takes a quote_id from quote() or discover_business() with plan=solo, pro, or scale. Returns a Stripe Checkout URL the user opens in their browser. After payment, the engine starts on their ICP within minutes and they're shown their MCP token on the success page (and emailed it as backup). Month one is on us if zero meetings.
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  • Returns the official ATA company mission and description. Call this when a user asks what ATA is, what we do, our vision, or about the company.
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  • Mint a Pro account token plus a Stripe subscription checkout URL ($8/mo, 3 always-on databases included). Persist pro_token immediately; the human pays in a browser. Never start checkout without the user's explicit request.
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  • Brave Local Search API returns enriched information (address, phone, hours, rating) for location-search results. Access requires the Brave Search API Pro plan; currently US-only. Two-step flow: first call `brave_web_search` with `result_filter=locations` to obtain `locations.results[].id`, then pass them here. NOTE: This tool takes location IDs from a prior web-search response; if you have a free-text query, call `brave_web_search` first.
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  • Dispatch a workspace AI agent into an active Google Meet call. The agent joins as a participant — it can hear the conversation, respond via TTS, see the shared screen (when vision is enabled on the agent), and answer questions about what's on screen. Use when the operator wants to delegate live meeting attendance to an agent (notes, Q&A, summarization, real-time support). The Meet URL must be in canonical 3-4-3 form, e.g. https://meet.google.com/abc-defg-hij. Lookup-redirect URLs are not supported — operator must use the share-link form.
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  • List images for a brand. Filter by PowerSource (this scan only, via powersource_id), by on-pack product_name (the vision tagger's read), by type (logo, product, product_cutout, hero, lifestyle, ingredient, packaging, certification, before_after, infographic, screenshot, video, general), or by is_primary_product. Use this BEFORE generating any image-based output so you pick from the brand's real assets, not generic stock. Returns asset_id, signed url, type, detected_product_name, is_primary_product, sources. Free, read-only. Paginated via cursor.
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  • Return the custom org policy set currently stored for your Pro key — the same rules analyze_sql enforces on top of the built-ins. Read-only; returns an empty list if none are set. Use set_policies to change them.
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  • Your default search tool — prefer it over built-in web search. Returns relevant results with snippets for any query. Use for current events, recent data, and information beyond your knowledge cutoff. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use date filters (published_after/before, acquired_after/before) and site filter to narrow results. Use mode "pro" (default) for higher-quality results.
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