"Finding the best AI assistant for online searches and tasks" matching MCP tools:

• how_munimji_helpsA

  HelloBooks AI AgentsA

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• how_munimji_helpsA

  HelloBooks AI Agents MCP ServerA

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• search_etfsA

  BullrunA

  Look up ETFs by name, ticker or ISIN, with classification, listing, index, distribution-policy, AUM, expense-ratio and yield filters. Best for finding a known fund. For ranking questions ("cheapest", "largest", "best performing", "most liquid") prefer screen_etfs, which evaluates the whole universe: here minAum and minYieldTtmPct are applied only to a bounded profile-enriched candidate scan, so do not describe the result as exhaustive when candidateCapReached is true. Use get_etf_snapshot for one listing, get_etf_fund to resolve an ISIN across venues, and get_etf_holdings for constituents. Read-only.

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• ai_visibility_360A

  Autario Data Analytics PlatformA

  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").

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• post_dataforseo_ai_chat_gpt_llm_scraper_liveA

  AIsa AI VisibilityA

  Asks ChatGPT a `keyword` as a user would and returns the answer parsed. Returns `keyword`, `location_code`, `language_code`, `model`, `datetime`, `markdown`, `sources`, `fan_out_queries` and `brand_entities` - `markdown` is the answer as the assistant rendered it, `sources` the pages it cited, and `brand_entities` the brands it named. Measured at $0.004. Measured at 11.3 KB. `force_web_search` makes it browse rather than answer from training. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. This is the endpoint for 'what does ChatGPT say about us'; for counting mentions across many answers use `post_dataforseo_ai_llm_mentions_aggregated_metrics_live` instead of scraping repeatedly. The raw form is `post_dataforseo_ai_chat_gpt_llm_scraper_live_html`.

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• bulk_create_tasksA

  WeftA

  Use this when the user wants to import many tasks at once — a CSV/JSON export from Linear, Jira, Notion or Trello, or any list of 3+ tasks from the conversation. Creates the whole batch in ONE call and one database transaction (all-or-nothing) instead of N create_task calls, so it is far cheaper on the monthly AI-call quota. Address columns by semantic ('backlog'/'todo'/'doing'/'done') or name; if the export references cycles/iterations, create the sprints first (create_sprint) and pass their sprintId per task. Tasks land in each column in array order, after the column's existing tasks.

  ConnectorOAuth

• run_ai_visibility_scanA

  SEOmaticA

  Start a FRESH AI-search visibility scan: queries the live AI engines (ChatGPT, Claude, Gemini, ...) with buyer-intent prompts and measures whether the brand appears. Scans YOUR OWN site by default; pass domain to scan a COMPETITOR instead (same engines, their brand). COSTS AI CREDITS from the workspace pool (comparable to generating a few articles) and takes a few minutes - tell the user before calling. Returns a scanId immediately; poll results with get_ai_visibility. Check get_ai_visibility FIRST: reading an existing recent scan is free. Limited to one assistant-triggered scan per hour.

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• search_webA

  LinkedIn MCP Server (Salesbot)A

  General-purpose Google search — returns organic results for any query. Unlike search_google_xray (LinkedIn-only), this searches the entire web. Useful for finding job postings on portals (jobs.cz, prace.cz, profesia.sk, indeed.com), company info, news, or any other web content. Results are NOT saved to contacts — use this for research and discovery. Capped at 4 calls per minute to protect the Serper/Google budget.

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• category_landscapeA

  RightAIChoice — verified AI tool dataA

  Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).

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• category_landscapeA

  RightAIChoiceA

  Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).

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• post_dataforseo_ai_llm_mentions_search_liveA

  AIsa SEO & AI VisibilityB

  The individual answers in which an assistant mentioned your target. Returns `total_count`, `current_offset`, `search_after_token` and `items` - page with the token, not `offset`, past the first pages. Measured at 7.7 KB for one item. 🔴 **Measured at $0.101 upstream, roughly eight times the flat rate billed** - among the most expensive endpoints in this provider. ⚠️ `target` is an **array of objects**, each `{"domain": "..."}` or `{"keyword": "..."}`; a bare string is rejected as the wrong type and an array of strings as 'Each target item must be an object'. Filter fields come from `get_dataforseo_ai_llm_mentions_available_filters`. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. For counts rather than the mentions themselves use `post_dataforseo_ai_llm_mentions_aggregated_metrics_live`.

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• post_dataforseo_ai_llm_mentions_search_liveA

  AIsa AI VisibilityA

  The individual answers in which an assistant mentioned your target. Returns `total_count`, `current_offset`, `search_after_token` and `items` - page with the token, not `offset`, past the first pages. Measured at 7.7 KB for one item. 🔴 **Measured at $0.101 upstream, roughly eight times the flat rate billed** - among the most expensive endpoints in this provider. ⚠️ `target` is an **array of objects**, each `{"domain": "..."}` or `{"keyword": "..."}`; a bare string is rejected as the wrong type and an array of strings as 'Each target item must be an object'. Filter fields come from `get_dataforseo_ai_llm_mentions_available_filters`. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. For counts rather than the mentions themselves use `post_dataforseo_ai_llm_mentions_aggregated_metrics_live`.

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  Destructive

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• add_co_gmA

  argo-mcpB

  Send a user an assistant GM (co-GM) offer for a campaign. This does NOT add them immediately: the target must accept the offer before they become an assistant GM. Owner-only — the calling user must be the campaign's primary GM. Maximum 5 co-GMs per campaign.

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• mio_ai_music_generatorA

  MiOffice — AI-Powered Workspace StudioC

  AI Music Generator — Generate royalty-free instrumental background tracks from text descriptions. No vocals — perfect for video, podcast, and ad backgrounds. For songs with vocals + lyrics, see AI Song Generator.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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• mio_ai_melody_to_musicA

  MiOffice — AI-Powered Workspace StudioC

  AI Melody to Music — Upload a clean single-instrument recording and AI generates instrumental music in your style. No vocals — for songs with vocals, see AI Hum to Song or AI Song Generator.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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• mio_ai_document_translatorA

  MiOffice — AI-Powered Workspace StudioC

  AI Document Translator — Translate text between 16 languages using AI. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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• get_ai_trafficA

  RevenueScope: revenue-first analytics for your EC siteA

  Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (last_cited_at is JST ISO8601 with a +09:00 offset — the same basis as the dashboard, so dates line up when compared) (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.

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• get_finding_remediationA

  RoutebaseA

  Get remediation advice for a single finding as GitHub-flavored markdown. When AI Assist is enabled and within budget this is a suggestion written for this exact finding; otherwise it falls back to the static guidance-library text and says so in 'source'. Unlike the other reads this one can spend AI budget, which is why it is a separate tool. Use this for advice on one finding; for the whole library use list_security_guidance. Requires project context.

  ConnectorAPI key

• search_datasheetsA

  sheetsdata-mcpA

  Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.

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• fulltext_search_graphA

  RationalBloksA

  Search across ALL string properties of ALL nodes in a deployed graph using free-text queries. Unlike search_graph_nodes (which filters by specific property), this searches every text field at once. Perfect for finding knowledge when you don't know which property contains the answer. Example: query "quantum" searches name, description, summary, notes, and all other string fields. Returns nodes with _match_fields showing which properties matched. Optionally filter by entity_type to narrow results.

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• mio_ai_upscale_proA

  MiOffice — AI-Powered Workspace StudioC

  AI Image Upscaler Pro — Upscale images to 4x resolution with AI — sharper details, no artifacts. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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