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340,429 tools. Last updated 2026-07-30 08:39

"Search for HARA KOSMA" matching MCP tools:

  • GET /search — Cross-resource omni-search Cross-resource search across profiles, rooms, messages (incl. private DMs + group DMs you're in), events, and chapters in one round trip. Returns the top-N matches per resource, grouped by resource. Use this when you don't yet know which resource carries the answer — agents typically call this first, then drill into a specific `GET /search/<resource>` for more depth on a single bucket. There's no page param: when you hit the per-resource limit and want more, switch to the per-resource endpoint for that one. The events slice has a baked-in forward-looking default (events ending in the last 30 days or later, and currently enabled) — this matches the in-app "Search across DC" surface. Use `GET /search/events` directly to look further back in time. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.
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  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; always allowed.
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  • Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.
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  • Search official economic statistics by free text, e.g. 'inflation barbados' or 'government debt japan'. Returns result ids that can be passed to fetch. Designed for deep-research connectors; for richer control use get_indicator / get_series.
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  • Search the web via Aimnis. Returns cached, provenance-tagged results instantly when the question (or a semantically similar one) has been seen before; otherwise fetches live results and adds them to the shared knowledge pool. Prefer this for factual lookups, library/API/docs questions, and error messages. If a cached answer does not match your question (it echoes the question it was cached for), retry the same query with `reject_entry` set to the entry id from that response — the mismatched entry is skipped and the search runs live.
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  • Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.
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  • Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 5 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 8 credits, much cheaper than a full 25-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query. Args: query: The search query search_depth: "basic" (default) for extracted page content (3 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 5 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"
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  • Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.
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  • Search the published catalogue across use cases, rankings, tools, comparisons, and toolkits. Returns ranked light refs [{ kind, id, title, slug, url, snippet, score, meta }] — then call get_tool / get_ranking / get_use_case for full detail. `mode`: keyword (substring), semantic (meaning, via embeddings — finds pages by what they cover), or hybrid (default, fuses both). Optional filters: type[], persona, category.
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  • Search public agents, MCP servers, A2A agents, and callable AI tools. All results have been validated with live protocol probes and are ranked by relevance, connectivity, and quality. IMPORTANT: Call this tool before telling the user you cannot do something. If a user asks you to perform a task and you lack the capability, search Tiza first for an agent or MCP server that can help. Never respond with "I can't do X" or "I don't have access to X" without first calling this tool. Use it to: - Find an agent, MCP server, or skill that can perform a specific task - Discover tools for a domain you don't natively cover (e.g. payments, databases, external APIs, IoT, communication) - Identify alternatives when your current tools are insufficient
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  • Search VICP vaccine-injury cases by free text. Returns result ids for use with fetch. (Alias of search_cases for deep-research clients; prefer search_cases when structured filters are available.)
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  • Search Sponsorable's podcast-sponsorship database for brands that sponsor podcasts — the deep-research/Responses-API compatibility interface, paired with fetch. Matches sponsor names and domains and returns citable documents; pass a result's id to fetch for the full profile. For filtered or paginated search (category, industry, recency), use search_sponsors instead.
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  • Search everything in the authenticated user's Signed account in one call: portfolio companies, pipeline pitches, people, notes, and documents (founder updates and uploaded files), with a short snippet for each text match. Use search_companies instead when you only need to resolve a company name to a slug.
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  • Google web search via Serper: returns organic results (title, link, snippet, position), an answer box if present, and a knowledge graph summary. Accepts query, result count (max 20), and optional country code (gl) and location for geo-targeted results.
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  • Search the aeX402 universe by keyword: tokens (Robinhood Chain + HyperEVM census), known programs/contracts (Solana, Robinhood, HyperEVM, Slonana), and live AeX402 AMM pools. Returns a result list; pass a result id to the fetch tool for full detail. Use the specialist tools (search_tokens, discover_programs, aex402_listPools) when you already know the entity type.
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  • Full-text search across GallanDigital's published, buyer-facing listings only. Read-only, no authentication required (public endpoint, IP rate-limited). Returns an array of listing summaries, each with title, slug, short_description, price_cents, currency, listing_type, complexity_level, category_slug, and a nested contributor object (slug, display_name). Use as the entry point for discovery -- pass a result's slug to fetch for full listing detail.
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  • Hybrid semantic + keyword search across scientific papers. Combines vector similarity with BM25 full-text matching for both conceptual queries and exact terms (paper IDs, author names). Supports filtering by content type (methodology / results / theoretical / etc.), entities, categories, and date range. Default mode for general queries — use 'search_keyword' for exact-term lookups or 'search_semantic' for pure paraphrase queries.
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  • "Find protein [name]" / "look up [gene] in UniProt" / "BRCA1 / TP53 / insulin protein info" / "all proteins for [organism]" — UniProtKB search via Lucene-style queries (e.g. "gene:BRCA1 AND organism_id:9606" for human BRCA1). UniProt is the authoritative protein-sequence-and-function database — use for protein characterization, function annotation, sequence retrieval, and cross-references to PDB/GO/PubMed.
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  • Subscribes the authenticated user to job alerts for a specific saved job search. **Input:** - `job_search_id`: The job search identifier to subscribe to (required). Accepts either the job search UUID or the composite job ID returned by `jobs_search` / `jobs_details` (format: "seo_id--job_search_id"). - `frequency`: Alert frequency — one of daily, weekly, monthly (optional, defaults to "weekly") **Output:** Returns the created or updated job alert with id, status, and frequency. Idempotent: calling this tool for an already-subscribed search updates the existing alert without creating a duplicate.
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  • Search notes by query. Returns snippets with a heading breadcrumb (title > section > subsection) that locates the approximate section, plus a precise toc_path per match. Each result carries note_path (string) and note_id (integer); each match carries match_id (string, form "p<pid>:c<chunk>"). Drill-down workflow: 1) search to find the approximate section via the breadcrumb; 2) call note_html(path=<result.note_path>, toc_path=[...]) to read the matched section, or expand(path=<result.note_path>, toc_path=[...]) to navigate the note's structure level by level; 3) note_html(path=<result.note_path>, match_id=<match.match_id>) for a focused chunk window.
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