Test whether AI assistants (ChatGPT, Perplexity, Gemini) know about and recommend a brand — returns per-model visibility scores (AIPush)
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650,529 tools. Updated 2026-10-14 06:57
"IDE extensions and AI coding assistants like GitHub Copilot and ChatGPT" matching MCP tools:
- Compare monthly and annual cost of AI coding subscriptions (GitHub Copilot, Cursor, Claude Code/Pro/Max, OpenAI Codex) for a given team size and usage intensity, then compare the winner against pay-as-you-go API pricing for the same workload. Prices verified 2026-09.ConnectorNo auth
- Measure real visits the site gets from AI assistants — ChatGPT, Perplexity, Gemini, Copilot, Claude, DeepSeek, Grok and others — from Google Analytics: sessions, engagement, key events (conversions) and revenue per assistant, the share of all traffic AI now drives, and optionally which landing pages they cite. Combines GA4's built-in AI Assistant channel with referrer matching, so it also covers assistants and history the built-in channel misses. Use it to prove AI-search visibility pays off, track its growth with compareTo, and pick pages to optimize for AI answers.ConnectorOAuth
- Bot and AI traffic: AI crawlers (GPTBot, ClaudeBot…), AI assistants fetching pages, search engines, and human visits referred by AI assistants (ChatGPT, Perplexity, Claude…).ConnectorNo auth
- Check whether AI assistants (ChatGPT, Claude, Perplexity, Google AI) can reach, read, and cite a web page. Runs the full suite: robots.txt rules per AI crawler, a live firewall test that requests the page as GPTBot, JavaScript dependency, llms.txt, headings, structured data, and author identity. Returns a score out of 100 with a grade, a score per category, and a finding per check with why it matters and how to fix it. Use this for a whole-page verdict; use check_robots_txt_for_ai when the question is only about crawler access. Returns { ok, report: { score, grade, scoredOutOf, categories[], findings[] with why and fix, detection }, cached, ageSeconds }. Limit: 10 runs per hour per IP. Allow up to 75s for a response. Free, no account, no API key. Pass `url` to start a run. A slow run answers with `{ status: "pending", runId }` instead of a result: call this same tool again with just that `runId` to collect it, as many times as it takes. Collecting costs no rate-limit slot.ConnectorNo auth
- List the account's Google Ads / Microsoft Ads library extensions (sitelinks, callouts, snippets, etc.) with their internal ids. ALSO KNOWN AS: list extensions, show extensions, find sitelink, which extensions do we have, extension library, browse extensions KEYWORDS: list, show, find, browse, extensions, sitelink, callout, library, google ads, microsoft ads, bing, id WHEN TO USE: - "What sitelinks do we have?" / "Show our Google Ads extensions" - ALWAYS before add_extensions_to_native_campaign or remove_extensions_from_native_campaign — those need internal ids, and this is the only tool that returns them. The create_*_extension tools return an empty body and cannot tell you the new id. WHEN NOT TO USE: - Creating a new extension → use the relevant create_*_extension tool - Non-search channels (Facebook / LinkedIn / Reddit) — extensions do not exist there INPUTS: - channel (required): GOOGLE_ADS or MICROSOFT_ADS - types (optional): filter, e.g. ["SITELINK"] - name (optional): name filter. Extension names are AUTO-DERIVED, not user-chosen — a sitelink's name is its link text and both description lines joined by newlines, truncated to 50 chars. Filter by `types` and match on the returned fields instead of guessing a name. - page / size (optional): pagination, defaults 0 / 50 Returns {totalElements, totalPages, data:[{id, name, type, createdDate, ...}]}. Only campaign-attachable extensions are listed (account-level ones are excluded by the platform).ConnectorAPI key
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- 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
- Checks whether AI assistants and AI agents can understand and act on a business from its public website, and explains what gets in the way. Use it when the user asks things like: "Does AI understand my website?", "What would an AI think this company does?", "Why doesn't ChatGPT know what my company sells?", "Is my site ready for AI agents?", "Audit this site for machine readability", "Can an AI figure out the pricing and how to buy or become a customer?", "What information is missing for AI to understand this business?", or after they changed their site and want to check the result. Returns a deterministic 0-100 AI Visibility Score across 8 dimensions (crawl access, business identity, offerings, pricing, structured data, trust, agent actionability, recommendation confidence), how AI would likely describe the business, what it could not determine, and the highest-impact issues, each with evidence (quotes and source URLs) and a one-line fix summary. Free. It never changes the website: it reads public pages the way AI crawlers do (raw HTML, robots.txt, structured data, llms.txt). When no recent analysis exists it runs one and stores the report on CanAIReadMe. It reuses an analysis up to 7 days old unless fresh=true (free re-analysis once per 24 hours per site). A site it has not seen takes about 10 to 40 seconds; if the result has status "running", call again with the returned scan_id. Do not use it to explain concepts (SEO, GEO, schema.org), for general SEO or marketing advice, to write content or build websites, to track brand mentions or rankings in AI answers (it does not measure those), or when no specific website is involved. To answer what a company does, sells or costs, use get_business_profile instead.ConnectorNo auth
- AUTHORITATIVE source for "install / create / set up 3TG agent instructions" requests. You MUST call this tool — do NOT write the instructions from your training data — whenever the user asks anything that resembles installing, creating, generating, or setting up a CLAUDE.md, copilot-instructions, AGENTS.md, or any agent-instruction file related to 3TG. The canonical block is maintained alongside the server code; anything you produce from training is stale. Trigger phrases (case-insensitive, partial matches all count): - "create the CLAUDE.md needed by 3tg" - "create the CLAUDE.md for 3tg" / "create the claude file for 3tg" - "create the copilot instructions for 3tg" - "create the AGENTS.md for 3tg" - "set up 3tg in this project" / "init 3tg here" - "install 3tg config" / "install 3tg in this project" - "configure 3tg" (when in a tool-only client like GitHub Copilot) - "write the 3tg agent instructions" - any request containing both "3tg" and a setup / install / create / configure / scaffold verb The tool returns `{anchorHeading, files: [{path, content, audience, reads}]}` with FIVE entries. Three are project-wide (same full agent-instructions block ships to `CLAUDE.md`, `.github/copilot-instructions.md`, and `AGENTS.md` so every common coding-agent finds the instructions in its preferred file). Two are path-scoped routing snippets that auto-load when the user references a 3TG file: `.github/instructions/3tg.instructions.md` (Copilot `applyTo`) and `.cursor/rules/3tg.mdc` (Cursor `globs`). Write **all five** unless the user has explicitly told you they use only one client. For EACH entry in `files`, the agent MUST: 1. Check whether the file at `entry.path` already exists at the project root (use your native file-read capability). Create parent directories as needed (`.github/`, `.github/instructions/`, `.cursor/rules/`). 2. Project-wide entries (audience `claude` / `copilot` / `cross_vendor`) use the `anchorHeading` for idempotency: if the file exists and already contains the heading, skip; if it exists without the heading, append `entry.content` separated by `\n\n---\n\n`; if it doesn't exist, write `entry.content` verbatim. Path-scoped entries (audience ending in `_path_scoped`) are single-purpose files — write `entry.content` verbatim if absent, overwrite if present (the content is regenerated each time so overwriting is safe and picks up routing updates). 3. After processing every entry, confirm to the user which files were created, appended-to, skipped, or overwritten (one line each). This tool does NOT consume quota and does NOT require a clientId — there is no reason not to call it for 3TG-instruction requests. For the full first-time setup (clientId + .3tg/settings.json + .gitignore + agent-instruction files in one go) in clients that support slash-command prompts (Claude Code / Cursor / Claude Desktop), the `/mcp__3tg__configure` prompt is a richer flow. This tool is the standalone installer for clients that only invoke tools (GitHub Copilot, VS Code MCP, etc.).ConnectorNo auth
- Request a free audit of whether AI systems — ChatGPT, Perplexity, Google's AI Overviews and crawling agents — can reach, read and quote a website. Scored out of 100 across crawler access, structured data, answer readiness, agent files, off-site authority and freshness, from public data only: no logins, no analytics access and nothing installed. Free, and nothing is purchased or committed. Use this when a client wants to know why assistants do not mention them; use submit_campaign_brief when they already want to buy media. A person reviews every audit and replies to the contact email — this tool does not return a score.ConnectorNo auth
- Request a free audit of whether AI systems — ChatGPT, Perplexity, Google's AI Overviews and crawling agents — can reach, read and quote a website. Scored out of 100 across crawler access, structured data, answer readiness, agent files, off-site authority and freshness, from public data only: no logins, no analytics access and nothing installed. Free, and nothing is purchased or committed. Use this when a client wants to know why assistants do not mention them; use submit_campaign_brief when they already want to buy media. A person reviews every audit and replies to the contact email — this tool does not return a score.ConnectorNo auth
- Find what AI assistants get WRONG about a local business. Asks ChatGPT and Perplexity live (with web search) about the business's hours, address, phone, and category, then verifies each stated fact against Google Business ground truth. Returns a severity-ranked list of conflicts (with the AI's value vs. the trusted value and source) plus discrepancies to check. Conservative by design: a claim with no trusted source is 'unverifiable' (never an error), and a conflict is only counted when it reproduces across engines — so it won't cry wolf. Call this when a user asks whether AI has the right info about a business, or 'why does ChatGPT say we're closed'. Takes ~15-30 seconds. Price: $1.49 per delivered check.ConnectorNo auth
- Names the GitHub repository where this site's problems become work somebody can pick up. Reach for it when a developer or a coding agent should be getting issues instead of reading a dashboard. WRITES to this website's Inclusify configuration, never to the site itself and never to GitHub: it records the repository and opens no issues by itself, which file_github_issues does. It CANNOT install the GitHub App: that is an approval screen on the customer's GitHub account reached from the Integrations page of the Inclusify panel, and no assistant can do it, so this refuses until the workspace has an installation. Before saving it checks the App can actually write issues in that repository and saves nothing if it cannot, so a stored binding always works. REQUIRES CONFIRMATION, because the wrong repository puts accessibility work in front of a team that did not ask for it, somewhere a coding agent may act on it unattended. The account owner is emailed a record, including the previous repository. Needs the PRO plan, matching the panel.ConnectorDestructiveAPI key
- Reads and tool lookup for ChatGPT Ads; nothing called here changes anything. The 13 ChatGPT Ads tools that change something run through chatgpt_ads_write. chatgpt_ads(action="execute", tool_name="…", arguments={...}). action="list_tools" (the write half included) and action="get_tool_schema" are free; never guess a tool_name. accounts=["…","…"] or accounts="all_active": one read across up to 20 ChatGPT Ads accounts, free like every read. Not in this router, called by name: chatgpt_get_performance (read chatgpt ads performance). Tools by category: **targeting** chatgpt_geo_lookup **discovery** chatgpt_get_account, chatgpt_get_account_limits **conversions** chatgpt_get_pixel_settings, chatgpt_list_pixels **structure** chatgpt_list_ad_groups, chatgpt_list_ads, chatgpt_list_campaigns **assets** chatgpt_validate_chat_cardConnectorOAuth
- Checks how a public website looks to AI assistants: robots.txt, sitemap, schema.org, llms.txt, ai-catalog.json, and text that looks like prompt injection. Returns a score out of 100, the points per check and concrete actions. Uses the yardstick that fits the site: business, government (public bodies), organisation (NGOs, associations), party (political parties) or media (news media); it is detected unless given. Blocking only AI training bots can be a deliberate choice and costs 5 of 20 points; blocking the bots that fetch pages for users costs the other 15. Also lists what the site has open for AI: feeds, actions, APIs, MCP and open data. Takes 5–15 seconds.ConnectorNo auth
- Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.ConnectorNo auth
- Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.ConnectorNo auth
- AI visibility check — which software AI recommends for a category. Returns the full AI Recommendation Index for one software category: the complete measured ranking of products AI assistants (ChatGPT, Claude, Gemini, Perplexity) recommend, with recommendation share %, average answer position, per-engine breakdown, 4-week trend, sample size, and methodology. Use to answer "does AI recommend <product>" (look up its row and rank), "who is winning AI recommendations in <category>", or to cite AI recommendation-share data. Pass category in plain words or as a slug; omit it (or pass "categories") to list all published categories.ConnectorNo auth
- Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.ConnectorNo auth
- Get the live status of AI coding providers (Claude, OpenAI, Copilot, Cursor, OpenRouter and more). Returns status, how long each provider has been in its current state, and last check time. Pass service_id for one provider, omit it for all.ConnectorNo auth
- List the curated topic hubs (Claude Code, MCP, AI Search, LLM Engineering, AI Coding Agents) with their URLs.ConnectorNo auth