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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510,032 tools. Updated 2026-09-03 17:32
"Information or meaning related to 'Tavily'" matching MCP tools:
- Find sections (stories) by TITLE. Exact words first (phrase, then AND-of-tokens); when NO title contains the words, falls back to MEANING matches in any language (hits carry match='meaning' + a cosine score — verify with get_section before relying on one). Each match includes `first_citation` and `last_citation` — ready-to-use canonical citations (e.g. 'M2:2608'). To read a whole multi-section story, call get_range with the FIRST match's `first_citation` as start and the LAST match's `last_citation` as end. Do NOT construct a citation from `first_beyt_global` — that is a GLOBAL index (1..25635), not a daftar-local beyt number. Example: find_sections('ابلیس معاویه', daftar=2).Connector
- Locks in the CURRENT terms of the negotiation exactly as they stand and creates a permanent, immutable Contract between the two agents. Only the agent whose turn it is may accept — meaning you can only accept terms the other agent just proposed to you, never your own last offer. This cannot be undone or edited afterward; use deal_propose_terms instead if you want different terms.Connector
- Verify a rewrite actually improved: score delta, meaning check, voice distance. PREMIUM (license). Compares reads-human score before/after, a meaning-preservation proxy, and (with a fingerprint) numeric distance to the target voice. Typical input {"original": "<draft>", "rewrite": "<edited draft>"} returns {"score_before": N, "score_after": N, "score_delta": N, "content_word_retention_pct": N, "remaining_tells": [...], "verdict": "Improved — ship it" | "Marginal — ..."}. Use only when both the before and the after text are available. Not for scoring a single draft (ai_tell_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "both texts must be non-empty"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.Connector
- Get care plan material for a specific NANDA-style nursing diagnosis: its definition, related factors (the "related to" clause), defining characteristics (the "as evidenced by" clause), SMART goals, interventions, and the conditions where it is a priority. Use when a nursing student asks about a diagnosis rather than a disease, for example "risk for infection", "acute pain", "impaired gas exchange", "ineffective coping" or "risk for falls", or asks how to write a three-part diagnosis or an AEB statement. Educational reference, not medical advice.Connector
- Use after vocabulary_index when the specific subject type does not yet exist. Submit terms broad-to-specific, for example ['food','recipe']. The server reuses existing dictionary entries, creates only missing provisional nodes in context, adds belongs_to relationships and rejects cycles. Do not include 'review': review is the record type, not a subject category. Semantic placement must be based on meaning, never on which review arrived first. Before creating a new semantic node, distinguish a genuinely different concept from a mere naming variant. Naming variants should reuse identity; genuine meaning differences may remain separate. Classification vocabulary should represent what a subject fundamentally is. Before creating, selecting, relating or proposing a subject type, identify the semantic head and descriptive modifiers. Material, arrangement/grouping, state/condition, quantity, colour, size, location and purpose/use normally belong in attributes or relationships rather than subject-type names. This is not a simplistic head-noun rule: a compound may remain a distinct type when the combined concept has materially different identity, behaviour, relationships, classification meaning or realistic retrieval needs. The server independently validates structural writes, so client guidance cannot bypass this rule.Connector
Matching MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.331MIT
Tavily MCP Serverofficial
AlicenseAqualityDmaintenanceThis server enables AI systems to integrate with Tavily's search and data extraction tools, providing real-time web information access and domain-specific searches.420,8992,359MIT
Matching MCP Connectors
Tavily MCP — wraps the Tavily API (tavily.com)
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- Search APIs by meaning and keyword across name, slug, and description, returning matches WITH their descriptions (schemas omitted), ranked most relevant first. Pass `query` (required), optionally `category` or `platform` to narrow, and `limit` to cap matches (default 25, maximum 50). Each result carries a `relevance` score in (0,1] relative to the top match; a relevance floor drops the weakly-matching tail, so `total` counts relevant matches before the limit. `ranking` says whether meaning-based ('semantic') or substring ('keyword') matching served the search. Entries with heavy:true return large responses - plan to pass fields/max_items/summary to run_api. Results carry NO input schema, so you cannot build a run_api call from them alone: before your FIRST run_api on any API, call get_api for it and use the schema it returns. Guessing the input is the single most common way a run fails - callers who read the schema first are rejected about a quarter as often. Use list_apis to browse everything.Connector
- Provides explanatory text for STRING features and limitations. Use this tool when the user question involves: - What is STRING is or how to use the tool (how_to_use_string, cytoscape) - functionality not available via MCP tools (e.g. GSEA, regulatory networks, large datasets). - meaning of the lines in the network (line_colors)Connector
- Get the full data dictionary for one dataset: prose plus every column's type, nullability and plain-English meaning. Use before read_dataset to choose columns.Connector
- 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.Connector
- Semantic (meaning-based) search over Teppek's live listings, backed by a multilingual embedding index. Use it when the user describes what they want in natural language rather than exact keywords — e.g. "outdoor jobs that don't need a degree", "affordable family cars that are good in snow", "a cosy studio close to the university". Returns the closest-matching ACTIVE listings ranked by semantic similarity (each item carries a `score`). Complements search_listings: prefer search_listings for precise keyword/role/country/category/price filtering and exact counts; prefer semantic_search when meaning, synonyms, or fuzzy intent matter more than literal terms. Optional `vertical` narrows to jobs/real_estate/vehicle/service.Connector
- Returns one published timeline. Administrators get the complete bilingual record with every event, source, and related link, plus access to draft content. Other accounts get a single locale (pass the caller's language in locale): each event's title, summary, media, sources, and related links, plus a canonical URL to the full timeline - never event bodies or the timeline introduction/conclusion.Connector
- Reference data: the ProShip order status pipeline. Returns every status code with its Thai label and English meaning. Use these codes with list_orders and update_order_status.Connector
- Metadata for one skill or journey: its definition, when to use it, when not to, and related slugs. Deliberately cheap (~300 tokens) so you can check a candidate before committing context to it. Pass sections to widen, or sections:["all"] for the whole page. This does not return the skill instructions — load_skill does.Connector
- Search APIs by meaning and keyword across name, slug, and description, returning matches WITH their descriptions (schemas omitted), ranked most relevant first. Pass `query` (required), optionally `category` or `platform` to narrow, and `limit` to cap matches (default 25, maximum 50). Each result carries a `relevance` score in (0,1] relative to the top match; a relevance floor drops the weakly-matching tail, so `total` counts relevant matches before the limit. `ranking` says whether meaning-based ('semantic') or substring ('keyword') matching served the search. Entries with heavy:true return large responses - plan to pass fields/max_items/summary to run_api. Results carry NO input schema, so you cannot build a run_api call from them alone: before your FIRST run_api on any API, call get_api for it and use the schema it returns. Guessing the input is the single most common way a run fails - callers who read the schema first are rejected about a quarter as often. Use list_apis to browse everything.Connector
- Call cc.auto_fetch_market_data — All-in-one market intelligence: OHLCV candles + computed indicators + Coinglass OI + news via Tavily. Everything an agent needs in one call. Purpose: All-in-one market intelligence: OHLCV candles + computed indicators + Coinglass OI + news via Tavily. Everything an agent needs in one call. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Responses may be cached (~300s). Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.008 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 15/min (per API key). Tier: premium. Returns: Multi-section response: technical indicators, OHLCV data, market microstructure (OI/funding), and relevant news summaries. Guidelines: Compute / parse / backtest only — no live orders. Feed outputs into cc.agent_strategy with force_paper=true to paper-trade. Tags: comprehensive, market-data, indicators, news, context, all-in-one.Connector
- Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.Connector
- Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). # get_ticket ## When to use Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). ## Parameters to validate before calling - case_number (string, required) — The ticket case number (e.g., "HYXTNJV")Connector
- [BROWSE] Map of the RRG 3D world, the spatial projection of the product embedding space that humans walk at /world. Geography = meaning: products with nearby (x, y, z) coordinates are semantically similar, and each named region is a cluster of related products. Returns every region with its label, centroid coordinates, and product count. Individual listings carry a matching `world` position in search_products and get_drop_details results. Also returns the VIA quarter: stores created on the VIA network (app.getvia.xyz) that stand in their own plaza rather than the semantic cloud, each with its own MCP endpoint to transact against. Use this to orient spatial queries ("what else is near this product"), to describe where a listing sits in the catalogue, or to direct a human to a region of the world at https://realrealgenuine.com/world.Connector
- SEMANTIC + ASSOCIATIVE search over the public sealed record and the published site corpus — the JYOTINT public brain (a neural associative memory: frozen deep encoder → Hopfield pattern completion → spreading activation over typed synapses → k-winners-take-all). Finds calls by MEANING, not keywords ('upper-stage anomalies' finds the calls that describe one without those words) and returns the RELATED subgraph, not just isolated hits. Retrieval-only and non-generative: every result is VERBATIM sealed/published text with public provenance (source URL, SHA-256 seal hash, frozen grade) plus an explainable why/activation path and Hopfield convergence info. Prefer this over search_sealed_forecasts for fuzzy/conceptual queries; the REST twin is GET /brain?q=…Connector