"Search for 'aperag' - unclear term or possible misspelling" matching MCP tools:

• search_translations

  Source Library

  Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.

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• semantic_search

  redm-mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings.

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• search_library

  Achriom

  Semantic search across the user's entire library by meaning, theme, or vibe. Searches every book/movie/album/show/anime as one corpus. Use for cross-media or thematic questions like "things about grief" or "noir mood". For specific title/creator lookups, use the keyword `search` tool instead.

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• get_api_operation

  Cannon Studio

  Return public docs for Cannon Studio developer API operations and payload shapes. Public read-only: no auth, no state changes, no charges; use this before estimate_generation_cost or create_generation_request when operation/input fields are unclear.

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_documentation

  Hatchable

  Search Hatchable's own documentation for platform behavior — routing, the SDK surface, deploy semantics, auth config, runtime limits. Call this instead of guessing when you're unsure how a Hatchable feature works. Ranks results by term frequency across headed sections. Returns source file, section heading, and a snippet around the hit.

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• search_nodes

  Diagrams MCP

  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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• cid10_search

  Medical Terminologies MCP

  Search the Brazilian CID-10 (Classificação Estatística Internacional de Doenças, 10ª Revisão) by Portuguese text. Use this tool to: - Find CID-10 codes for Brazilian SUS / ANVISA contexts ("infarto", "diabetes", "tuberculose") - Look up the official Portuguese (CBCD/USP) translation of a clinical term - Locate codes for billing, epidemiology, and clinical documentation in Brazil Returns matches from CID-10 categories (3-char) and/or subcategories (4-char). Search is diacritic-insensitive: typing "infeccoes" matches "infecções". This tool searches the Brazilian Portuguese CID-10 V2008 — for the international ICD-11 (current WHO revision, in English by default), use icd11_search.

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_data

  Opendata Ademe

  Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.

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• search_mesh

  PubMed / MEDLINE Literature Search

  Search the MeSH vocabulary for standardized medical terms. Find MeSH (Medical Subject Headings) descriptors to use in precise PubMed searches. Returns MeSH IDs, preferred terms, and scope notes. Args: term: Search term (e.g. 'diabetes', 'heart failure', 'opioid'). limit: Maximum results (default 10).

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• asterwise_get_tarot_yes_no

  Asterwise

  Draws one card and returns a yes, no, or maybe answer with confidence level. The answer is derived from the card's built-in yes_no polarity and its orientation. SECTION: WHAT THIS TOOL COVERS Quick binary oracle using the classical tarot yes/no system. Each card in the Rider-Waite-Smith deck has a pre-assigned polarity (yes/no/maybe). Reversal introduces uncertainty — a yes-polarity card reversed becomes maybe rather than no. This allows nuanced answers: strong yes, leaning toward yes, leaning toward no, strong no, or genuinely unclear. Answer logic (exact): yes-polarity card + upright → answer='yes', confidence='strong' yes-polarity card + reversed → answer='maybe', confidence='leaning' no-polarity card + upright → answer='no', confidence='strong' no-polarity card + reversed → answer='maybe', confidence='leaning' maybe-polarity card (any orientation) → answer='maybe', confidence='unclear' SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_tarot_three_card_spread — for more context when the yes/no answer is 'maybe' or the situation needs elaboration. SECTION: INPUT CONTRACT allow_reversed (bool, default true) — Recommended to keep true for nuanced answers. Set false only if you want strictly yes/no with no maybe results from reversal. question (optional string, max 500 chars) — The yes/no question being asked. Example: 'Should I accept this job offer?' Example: 'Will the project launch on time?' SECTION: OUTPUT CONTRACT data.card — full card object data.is_reversed (bool) data.answer (string — 'yes'|'no'|'maybe') data.confidence (string — 'strong' when card directly says yes/no; 'leaning' when reversed card; 'unclear' when maybe-polarity card) data.active_meaning (string — orientation-appropriate interpretation) data.question (string or null — echoed) SECTION: RESPONSE FORMAT response_format=json — full yes/no result object. response_format=markdown — formatted oracle response. SECTION: COMPUTE CLASS FAST_LOOKUP — cryptographic randomness, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_three_card_spread — positional reading, not binary answer. asterwise_draw_tarot_cards — free draw without answer logic.

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• contact_search

  DataMerge MCP

  POST /v1/contact/search. Search for contacts at specified companies. Returns a job_id (async, 202). enrich_fields required (at least one of contact.emails or contact.phones). Use company_list (slug) instead of domains to search a saved list.

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• find_trails_near_location

  AllTrails

  Find hiking, running, biking, backpacking or other trails for outdoor activities near a set of coordinates within an optional specified maximum radius (meters). Use this tool when the user: * Requests trails near a specific point of interest or landmark. * Requests trails near a named location within a specified radius or accessible within a specified time constraint. * Provides specific latitude and longitude coordinates. For most named places, use the "search within bounding box" tool if possible. Use this tool as a fallback when the bounding box of the named place is unknown. Users can specify filters related to appropriate activities, attractions, suitability, and more. Numeric range filters related to distance, elevation, and length are also available. These filter values MUST be specified in meters. In the response, length and distance values are returned both in meters and imperial units. These MUST be displayed to the user in the units most appropriate for the user's locale, e.g. feet or miles for US English users.

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• biobtree_search

  BioBTree

  Search 70+ biological databases. SYNTAX: biobtree_search(terms="entity") BEFORE SEARCHING - Use your training knowledge to plan: 1. What type of entity is this? (disease, process, drug, gene, protein) 2. What is the query asking for? (drugs, genes, function, etc.) 3. What equivalent terms might give better results? (e.g., "temperature homeostasis" is a process → related condition is "fever") 4. Choose best entry point for query type (disease terms for drug queries) WORKFLOW: 1. Search WITHOUT dataset filter first (discover where entity exists) 2. Use IDs from results with biobtree_map QUERY PATTERNS (choose based on question): "DRUG FOR DISEASE/CONDITION X": - Prefer disease terms (mesh/mondo/efo) over GO terms for drug queries - If search only returns GO term, search for the related CONDITION instead (e.g., "temperature homeostasis" → search "fever" instead) - Search disease → mondo → clinical_trials → chembl_molecule - OR search drug class directly (e.g., "antipyretic", "NSAID", "antibiotic") - Verify mechanism for top 2-3 drugs only (don't enumerate all proteins!) "DRUG TARGETS" (use BOTH paths for complete picture): - chembl: >>chembl_molecule>>chembl_target>>uniprot (mechanism-level) - pubchem: >>pubchem>>pubchem_activity>>uniprot (protein-level, often 50+ targets) - Filter approved: >>chembl_molecule[highestDevelopmentPhase==4] "DISEASE GENES": - Search disease → mondo/hpo → gencc/clinvar/orphanet → hgnc "PROTEIN FUNCTION": - Search protein → uniprot → go/reactome "MECHANISM QUERIES" (drug-disease): - Use biobtree_entry to see what's connected (xrefs) - Check EDGES to see where each xref leads - Follow connections relevant to your question - Build chain: Drug → Target → [connections] → Disease RETURNS: id | dataset | name | xref_count

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• search_rentals_natural

  arcasos-rentals

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

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• search_data

  Opendata Koumoul

  Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.

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• browse_collections

  Remember Me Collections

  Browse published Bible verse collections. Search by keyword, filter by language, sort by popularity. Args: search: Search term to filter by name, description, or publisher name. language: Language code prefix (e.g. "en", "de", "ja", "zh"). ordering: Sort order: -downloads (default), -created, name. limit: Number of results (1-100, default 20). offset: Starting position for pagination.

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• assess_location_aq

  Hermes — Air Quality Intelligence

  Comprehensive air quality assessment for a location in one call. Combines nearby monitor discovery and current readings with DAQI into a single response. Use this as the first tool call for any air quality question about a location. For long-term trend analysis, use the dedicated `trend_analysis` tool. Returns a structured 'summary' dict with purpose-appropriate sections. Present the summary description to users first. Args: location: Postcode, place name, or "lat,lon". purpose: What the user needs — "general" (default), "health" (safety/worry), "exercise" (outdoor activity), or "planning" (homebuying/school assessment/long-term).

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• semantic_search

  RedM Mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings.

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