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510,487 tools. Updated 2026-09-04 04:00

"General search for the term 'Agent'" matching MCP tools:

  • Search for diagram nodes by keyword across all providers and services. For targeted browsing when you know the provider, use list_providers -> list_services -> list_nodes instead. Args: query: Search term (case-insensitive substring match). Returns: List of matching nodes with keys: node, provider, service, import, alias_of (optional). Sorted by relevance: exact match first, then prefix, then substring.
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  • Look up the 99 Names of Allah (Asma ul Husna). Returns Arabic, transliteration, English and Bengali. Give a number for one name, a search term to match by meaning or transliteration, or neither to get all 99.
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  • Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 **Hints for the AI client:** The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to **Romanised Pāli (preferred) or English** before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. 🔍 **Pick the right search tool for the question shape:** - **Term lookup (exact word appearances)** — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - **Concept search ("discourses about X")** — e.g. "discourses about mindfulness of breathing": **use `search_hybrid` instead.** Canonical Pāli has two quirks that hurt keyword search for concepts: • Section headings (`Ānāpānapabba`) often use a different word than the teaching body, which uses verb forms (`assasati`, `passasati`, `dīghaṁ`, `rassaṁ`). E.g. DN22's Ānāpānapabba has 16 segments but the word `ānāpāna` appears in only 2 (header + footer) — the actual teaching segments won't match. • Stock phrases (e.g. `So satova assasati, satova passasati`) recur in 10+ suttas, so a keyword query ranks broadly and won't pinpoint the canonical reference. - **General keyword survey** — set `limit≥30` and filter client-side, or call multiple related forms (root verb + noun + compound).
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  • Anonymous bug report / feature request / docs gap, queued for human review. Default routing: `public-feedback` inbox (general AILANG). Pass `package="vendor/name"` (e.g. "sunholo/auth") to route to that package's `pkg:vendor/name` inbox where its autonomous agent watches. Categories: bug, feature, docs, limitation. Body limit 10KB, snippet limit 4KB. Optional contact field for follow-up; opaque to the server. Set `auto_dispatch=true` to authorize the package agent to act on your submission immediately (default false — files for human triage; pkg-feedback agent template lands in a separate sprint).
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  • List your org's TAGGING RULES (the dashboard's 'Tagging rules') — labels applied to posts your Watchers already ingest. NOT the dashboard's Keyword Monitor: for the keywords that search all of Reddit daily, use keyword_monitor_list. Each rule tags matching Dataset records whose title or body mentions its term as a whole word. Returns the term, active status, and match statistics. Changes take effect on the next scheduled processing cycle. Existing opportunity scores and matches are not retroactively updated. (requires a free Prowlo account — call it to get a signup link)
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  • Retrieve proteins annotated with a functional term or descriptive text in a single species. You can query for tissues, compartments, diseases, processes, pathways, and domains. IMPORTANT: For cross-species comparisons, run this tool separately for each species. Select relevant model organisms to search or ask user to provide the selection. The results reflect annotation depth within each category; use caution when interpreting. If no results are found, try simplifying the query. For tissue queries, follow BRENDA tissue nomenclature and omit the word "tissue" (e.g. use "skin" instead of "skin tissue"). Output fields: - category: Source database of the matched functional term (e.g. GO, KEGG, Reactome, Pfam, InterPro). - term: Exact identifier for the functional term. - description: The free text description of the term. - proteinCount: Number of proteins annotated with that term - preferredNames: Full protein-name list when `detail_for_term` is set - stringIds: STRING protein identifiers when returned - preferredNames_omitted: True when a row omits the protein-name list - stringIds_omitted: True when STRING identifiers are omitted
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Matching MCP Servers

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    MCP server exposing four task-shaped tools (resolve, pay, verify, disclose) for General Liquidity, enabling agents to normalize counterparties, submit intents, verify disclosures, and produce signed disclosures.
    MIT
  • A
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    A self-hosted, MCP-native web-search backend for AI agents that provides meta-search, clean extraction, RAG with citations, and GitHub project selection.
    2
    MIT

Matching MCP Connectors

  • Web search for AI agents. Ranked results with page passages already extracted, plus URL to markdown.

  • Live web search and research synthesis for agents, with free samples and x402 USDC payments.

  • Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.
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  • Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.
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  • The demand rollup a producer agent polls to decide what to write: `unmet` is the terms whose latest agent search found NOTHING (unanswered demand, gated by a distinct-searcher floor and a two-day spread), `top` the most-searched terms whose latest search DID match, and `windowDays` + `source` + `minSearchers` are the criteria that produced both, so a count arrives with its denominator instead of bare. Keyless and anonymous; it carries no term the public /trending page does not already show, and never a per-searcher field. The rollup is recomputed at most every 5 minutes and served through a shared cache that can hold it ~20 minutes worst case, so polling faster than that returns the same window. Complements `search`: a MISS tells you nothing answered YOUR question, this tells you what other agents are failing to find — either is a prompt to publish_essay.
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  • Search products by a term Arguments: term - the search term to look for products. It should be at least 3 characters long. cursor - optional, used for pagination. If provided, it will return the next page of results after. Pagination: Supports pagination with 'cursor' arguments. If 'cursor' is not provided, it will return the first page of results. Value for 'cursor' can be obtained from the 'nextCursor' field in the response. If 'nextCursor' is null, it means there are no more results to fetch. If value of cursor is null (or a string representation of 'null') dont send it in the payload. Results: Each product includes 'requiresFileUpload'. When true, the product has a required file-upload option (e.g. "upload your design") and shouldn't be added to cart through this assistant. Do not attempt to purchase it — tell the user it must be ordered on the website. Flow: - Call this tool with a 'term' argument and optionally with 'cursor' to search for products. - if you find a matching product, call 'get_product_details' with the product ID to get its variants and options (if any). - if 'requiresFileUpload' is true, inform the user the product needs a file upload and cannot be purchased here. - Call 'add_item_to_cart' with results of 'search_products' and 'get_product_details' (variant) tools to add the product to the cart. - [IMPORTANT] If product has variants ask user to pick
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  • Recommended first step for open-ended or topic discovery: free-text search across 14.5 million Smithsonian objects, with optional exact filters. Filters narrow by museum unit, object type, indexed date term, culture, geographic place, subject topic, named party, and online/CC0 availability. Returns curated summaries (title, date, museum, thumbnail URL, CC0 flag) with the total match count. The record_id in each result is the identifier for smithsonian_get_object, smithsonian_find_related, and smithsonian_get_media. To browse one exact category — a single museum, culture, date term, object type, or topic — use smithsonian_browse_category instead.
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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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  • Search NVD for CVE vulnerabilities by product or component name. Returns CVE ID, description, severity, and CVSS score. Search terms are matched against CVE description text and EVERY word must appear, so pass the product name ("OpenSSL", "log4j", "nginx") optionally with a technical term ("buffer overflow") — not a plain-English question. Use when researching security threats or checking if a known vulnerability affects your systems.
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  • Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata). Search results are ordered by a relevance score based on the FNI (Free2AITools Nexus Index) and, where term-match data is available, how well the entry matches the query. The score used for ordering may differ from the fni_score field returned in the response. The result set is bounded. The FNI is a 5-factor score: Semantic relevance, Authority, Popularity, Recency, Quality. The Semantic factor is a query-time baseline, not a live per-entity measurement (fni_s is returned null with a note). USE WHEN you need to discover which AI entities exist for a topic or keyword. DO NOT USE for general web search, to run/call/execute a model, to get a generated or inferred answer, or to route to an inference provider — this returns catalog metadata only, for the calling agent to reason over and decide on. Free discovery catalog: results are never paid placement / sponsored, and there is no billing or payment. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_select_model instead when you have specific hardware or license constraints.
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  • Return CalmActiva's curated CBD FAQ (legality, onset time, lab testing, shipping, brand disambiguation). Use for general CBD/brand questions before falling back to web search.
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  • Exact Google Ads search volume for `<keyword>` — Google's own monthly search-volume numbers (plus competition and CPC) from the Ads API, for up to 10 keywords. Use when you specifically need Google Ads figures; for general SEO volume + keyword difficulty, prefer seo_keyword_overview (cheaper). Example: seo_keyword_google_ads_volume({ keywords: ["running shoes"], location_code: 2840, _apiKey: "your-base64-key" })
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  • Keyword/full-text search over the Canton Network knowledge base (CIPs, Canton/Daml/Splice docs, forum, mailing lists, whitepapers, grant proposals, blog, YouTube, GitHub). Canton-specific. Do NOT use for other blockchains, the web, or local files. Use this for exact-term/name lookups; use semantic_search instead for conceptual or 'how does X work' questions, and get_doc to read a full page once you have its id. NOTE: forum matches cover the topic TITLE and the FIRST POST only; a term that appears only inside a forum reply will not surface here, so use semantic_search (which indexes all forum post bodies) when a forum discussion is likely and this returns nothing.
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  • Pure keyword (BM25) search — fastest option, optimal for exact-term lookups: paper titles, author names, method names (e.g. "LoRA", "RLHF"), arXiv IDs. Does NOT use semantic vectors. Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".
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  • Get the coding conventions Moxie inferred for the repository. Read-only; no side effects. Returns a Markdown list grouped by category (e.g. testing, structure, docs, review); each convention has a title, summary, confidence score, agent guidance, and the source file paths that evidence it. Use this for the general rules to follow; when you already know the files you're about to edit, prefer moxie.get_doc_impact for conventions scoped to those paths.
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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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