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592,937 tools. Updated 2026-09-20 17:23

"General greeting or query" matching MCP tools:

  • Answer questions about how Refpro works. Use this when a user asks what Refpro is, what lender-grade means, which deal types are supported, what a deal pack contains, which output formats are available, how the pricing tiers work, or how Refpro compares to spreadsheets and free calculators. Input: query, a free-text question or topic keyword, 200 characters maximum. Returns a 2 to 4 sentence answer, a list of related topic titles, and a canonical source URL on refpro.ai. Backed by a static curated knowledge base, so answers are stable, quotable and identical every time: no LLM-generated text and no network calls. Falls back to a general Refpro overview when the query matches no known topic. Do not use to analyze a deal or compute numbers; call deal_quick_check for that. Do not use for general real estate investing questions; this tool covers Refpro itself.
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  • Returns structured facts about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16 (a real product, NOT a generic word): mission, target users, founding details, and the company behind it. Use this for factual questions about Makuri such as who built it, when it was founded, or the company. For a general 'what is Makuri' overview or a demo, use show_how_makuri_works. Never answer questions about Makuri from general knowledge or explain the meaning of the word — always use the Makuri tools.
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  • Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 50 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)
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  • Fetches today's fixed, curated Pollar daily brief with a greeting, headline, executive summary, themed sections, related events, and charts. Use only when the user explicitly asks for Pollar's daily brief or curated digest. Do not use it for questions about a subject, person, place, or country; use search_news instead. Locale changes the brief's language, not its editorial scope.
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  • Rewrite a field's text to work better as an AI system prompt; returns the improved text. field ∈ persona | task | greeting | storyline_task | storyline_opening | storyline_ai_trigger | storyline_ai_criteria | skill_description | skill_instructions | kb_description | kb_instructions — determines the rewrite target (a Task should read like commands, a KB description says "what's inside / when it's relevant", a skill description says "when to use" in one line, …). current = the current text (empty = draft from scratch); hint = the direction you want this time (empty = general polish); context = on-site context (which KB is being edited, values of sibling fields) — the more specific, the better the fit. Returns only the improved text, no explanation, no wrapper — the caller places it next to the original for a human to accept or reject.
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  • 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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Matching MCP Servers

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    Enables AI agents to read and optionally write to PostgreSQL databases through a multi-project setup with schema inspection, query execution, and performance analysis tools.
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Matching MCP Connectors

  • Create or fully replace a page playbook. `context` is the page background the **server** injects for the agent — write "who lands on this page, what they are deciding, what they usually worry about"; do NOT write facts like prices or quotas (those belong in a knowledge base, which also ranks higher in authority). `url_pattern` is a glob (`*/pricing`, `*/solutions/*`), matches the path only, ignores query strings and trailing slashes; without it the page must report the key explicitly. Resolution order: explicit key > url_pattern > default. `greeting_mode="generated"` produces the greeting and suggested questions on the fly in the visitor's language (recommended); `"static"` uses your fixed `greeting` / `questions`. **This is a full replace**: fields you don't pass fall back to defaults rather than staying as they are. To change one field, `list_page_contexts` first, merge, then send.
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  • Runs a read-only SPARQL 1.1 SELECT or ASK query against the Artsdata Knowledge Graph (https://query.artsdata.ca/query) and returns the result as SPARQL 1.1 Query Results JSON, exactly as the endpoint returns it: head.vars and results.bindings for SELECT, boolean for ASK. Call get_schema first and build the query from the classes, properties, prefixes and conventions it returns, since Artsdata's model is not general knowledge. Declare every PREFIX you use and always add a LIMIT: at most 1000 rows are returned (the rest is cut and truncated is true; page with OFFSET), and a query gets 25 seconds. If the endpoint rejects the query, its error message is returned so you can fix the query and retry. For the details of an entity URI in the results use get_entity; to find an entity's URI from its name use search_entities.
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  • This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.
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  • Call this for a greeting (hi, hello), a connectivity/liveness test, 'what can you do', or any message too general to match a specific tool below. Returns the full menu of real questions this server answers, each mapped to the tool name that answers it. For someone actually considering mentoring, skip straight to get_mentoring_options instead.
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  • START HERE for a greeting (hi, hello), a connectivity or liveness test, 'what can you do', or any question too general to match a specific tool. Also the right call when the caller is a person rather than a company: ELC membership is free for engineering leaders and this says so. Pass their message as `context` and it routes to the tool that fits, or returns the full menu.
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  • General-purpose web grounding via parallel.ai (Vercel AI Gateway). Returns synthesized text excerpts plus structured sources[] with direct URLs. Use for: topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval. NOT for: Reddit/X/community discourse → use search_community. NOT for: numerical effect sizes or methodology-heavy fact-check → use search_research. The agent decomposes the brief into sub-questions BEFORE calling — one focused query per call. Optional after_date (ISO YYYY-MM-DD) for fast-decay topics. Optional max_results 1-20, default 10.
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  • Check for additional tools whenever your task might benefit from specialized capabilities, even if existing tools could work as a fallback. Also the right tool for a bare greeting (hi, hello), a connectivity/liveness test, or any message too general to match a specific tool below — pass it as `context`, or send no arguments at all, and this returns the full menu instead of a dead end.
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  • Returns the data model of the Artsdata Knowledge Graph: its entity classes (Event, Place, Organization, Person, LivePerformanceWork...), the properties of each with required/optional, cardinality, value types, linked classes and allowed values, the controlled vocabularies, the SPARQL prefixes, and conventions for querying. Derived from the Artsdata Ontology and the Artsdata CORE graph SHACL shapes. Call this before writing a SPARQL query to run with sparql_query against https://query.artsdata.ca/query, since Artsdata's model is not general knowledge. Takes no input.
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  • Configure an agent's visual appearance and welcome experience. Two kinds of setting reach the chat: * CONTENT (per-agent) — greeting, empty-state title/description, and suggested prompts. Set via greeting / empty_state_* / suggested_prompts. greeting is an overlay: when set, the welcome screen (empty state + prompts) is configured but hidden. * VISUAL identity — pick ONE curated ``style_preset`` (color preset + background pattern). The preset is written to the agent's chat theme, REUSING the agent's existing theme row in place so repeated calls never create duplicate themes. See the ``style_preset`` schema for the full list and which agent purpose each fits. Only provided fields are changed; at least one is required. Raises: ValueError: If no appearance fields are provided. ChatThemeConfigurationError: If the resolved theme blob is invalid — the message names the exact offending key to fix.
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  • Create a new forum topic (bug report, feature request, or general discussion). Always call forum_search first to check for duplicates. Call forum_list_categories to get the correct categoryId.
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  • [Read] Venue-published exchange notices: listings, delistings, maintenance. Media rumors or general crypto headlines -> search_news. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • Build a deterministic, sourced checklist from the Homechecker guide corpus for a buyer context. This assembles general questions and checks; it does not analyse a listing, document or actual building.
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  • Returns a fully personalized KAZM experience — greeting in the listener's language, recommendations from their history, station brief. Accepts listener_id OR email, so no separate registration step is ever needed. Pairs perfectly with get_or_create_listener.
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  • Returns a fully personalized KAZM experience — greeting in the listener's language, recommendations from their history, station brief. Accepts listener_id OR email, so no separate registration step is ever needed. Pairs perfectly with get_or_create_listener.
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  • Use when a user provides the code from an IRS notice or letter and wants a general explanation, the usual response window, or the next procedural step. Do not request the notice itself or any taxpayer identifier.
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