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

• discovery_estimate

  Disco

  Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.

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• search_products

  Graffeo Coffee Roasting

  Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Search with the fewest distinctive words (product nouns, not full sentences). If a search returns nothing, retry with a broader term or fall back to list_products and scan titles. Only sellable products are returned (drafts/archived are excluded). Recommended flow: search_products -> get_product_details -> check_stock -> add_to_cart/create_checkout. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

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• estimate_generation_cost

  Cannon Studio

  Estimate credits for a Cannon Studio generation request before creating billable work. Requires OAuth or a developer API key; it may update key/token usage metadata but does not spend credits, enqueue jobs, or change assets. Use get_api_operation first if operation or input fields are unclear, then pass the same operation/input pair to create_generation_request after user approval.

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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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• 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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• search_products

  Agentic Shelf

  Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Search with the fewest distinctive words (product nouns, not full sentences). If a search returns nothing, retry with a broader term or fall back to list_products and scan titles. Only sellable products are returned (drafts/archived are excluded). Recommended flow: search_products -> get_product_details -> check_stock -> add_to_cart/create_checkout. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

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• smithsonian_list_terms

  smithsonian-mcp-server

  Enumerate the valid term vocabulary for an indexed Smithsonian filter field (unit_code, culture, place, date, online_media_type). Terms are a controlled vocabulary — often plural or qualified (e.g. "Paintings", not "Painting") — so guessed filter values tend to return nothing. Returns a page of the field's distinct term values; large vocabularies (place has 100k+ terms) page via start and rows.

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• search_products

  PuroAir

  Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Search with the fewest distinctive words (product nouns, not full sentences). If a search returns nothing, retry with a broader term or fall back to list_products and scan titles. Only sellable products are returned (drafts/archived are excluded). Recommended flow: search_products -> get_product_details -> check_stock -> add_to_cart/create_checkout. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

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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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• browse_collections

  Remember Me Collections

  Browse published Bible verse collections. Search by keyword, filter by language, sort by popularity. Each result includes the collection's raw cover `image` — the URL the publisher set, or null if they set none (the app may still show an auto-generated cover when null). This is the stored value, not the computed display image. 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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• list_states

  GTH Intelligence - Substance Abuse Treatment Finder

  List every US state that has treatment facility data in this directory, with per-state facility counts. Returns an array of {state, stateAbbr, count}. Use as a first step when the user's location is unclear, or to discover where coverage exists before calling search_facilities.

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• get_vehicle_details

  FINN Auto - Car Subscription

  Get full specifications, equipment, all images, and pricing per term for a specific vehicle. Use a vehicle_id from search_vehicles results. IMPORTANT: Always show `detail_url` as a clickable link — it points to the FINN configurator where the user picks term and km. To produce a direct checkout link for a specific term + km combination (and optionally a one-time Fahrzeugbereitstellung), call `get_subscription_pricing` and use the `checkout_url` it returns. Never construct checkout URLs yourself. The `vehicle_id` field is an internal API identifier — never display it to users.

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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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• legal_list_laws

  Lawbster

  List available laws, regulations, and court decisions in the database. Returns abbreviation, title, source type, jurisdiction, document kind, and version date for each entry. Unfiltered listings can contain thousands of entries; pass a search term or source_type to keep responses focused. Useful for discovering valid law abbreviations to use as filters in legal_search. Found a relevant law? Use legal_get_toc to browse its structure. NOT an existence check for a specific law: EUR-Lex entries store the official long title, so searching by common name or number can miss laws that ARE in the corpus. To verify a law exists, use legal_lookup with a citation or legal_search with a topic instead.

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• analyze_land

  AcreLens

  Generates a comprehensive land analysis report for a US property through one of four analytical lenses: off_grid, rural_residential, recreational, or investment. Call this when the user asks for a full analysis of a specific property. If the user's intent is unclear, ask which mode to use before calling. Returns a report ID and poll URL — the final structured report (scores, confidence ratings, narrative summary, source citations) is delivered asynchronously via polling or webhook. Consumes one analysis credit from your AcreLens account.

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• get_related_terms

  Arco Lexicon

  Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.

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• get_sources

  Arco Lexicon

  Returns all published Arco sources for a term — Lexicon entries, blog articles, wiki pages, and podcast episodes — ordered by recommended reading sequence. Read-only. Use this when you need a reading list or reference list for a term. Use cite_term instead when you need a formatted citation for a specific publication type.

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• string_enrichment

  STRING Database MCP Server

  This tool retrieves functional enrichment for a set of proteins using STRING. - If queried with a single protein, the tool expands the query to include the protein’s 10 most likely interactors; enrichment is performed on this set, not the original single protein. - For two or more proteins, enrichment is performed on the exact input set. - When calling related tools, use the same input parameters unless otherwise specified. - Focus summaries on the top categories and most relevant terms for the results. Always report FDR for each claim. - Report FDR as a human-readable value (e.g. 2.3e-5 or 0.023). - IMPORTANT: Remember to suggest showing an enrichment graph for a specific category of user interest (e.g., GO, KEGG) - Very large responses are capped while preserving category diversity. - Use `expand_category` to return only one category with expanded term coverage and per-term gene details. - If a row has `preferredNames_omitted: true`, do not infer which proteins are in that term from the returned rows. Use `string_functional_annotation` with the same proteins/species and `detail_for_term` set to the exact term ID. Output fields (per enriched term): - category: Term category (e.g., GO Process, KEGG pathway) - term: Enriched term (GO ID, domain, or pathway) - number_of_genes: Number of input genes with this term - number_of_genes_in_background: Number of background genes with this term - ncbiTaxonId: NCBI taxon ID - preferredNames: Canonical protein names, only when the full per-term list is short enough to show - proteinCount: Number of proteins matching this term - preferredNames_omitted: True when the gene list was omitted instead of showing a misleading partial list - p_value: Raw p-value - fdr: False Discovery Rate (B-H corrected p-value) - description: Description of the enriched term Response metadata: - input_gene_name_mapping: Only included when displayed gene lists contain submitted identifiers that differ from STRING preferred names. - category_summary: Total and returned term counts per category; use `expand_category` for categories where `truncated` is true or where the user wants deeper category-specific detail. - truncated_categories / omitted_categories: Categories with terms not shown in the current response.

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• string_functional_annotation

  STRING Database MCP Server

  This tool retrieves curated functional annotations for a set of proteins. Each input protein is mapped to known biological terms from ontologies, pathway databases, tissues, compartments and domains — such as Gene Ontology (GO), KEGG, and UniProt Keywords. - Use this when the user asks what a protein does, where it's localized, expressed, or which pathways it participates in. - Keep the output short and focused by highlighting a few diverse and specific annotations for each protein. - This tool does not perform statistical enrichment — use the enrichment tool for that. Output fields (per protein): - stringId: STRING protein identifier - preferredName: Gene name or alias - annotation: Functional description or keyword - category: Source category (e.g. GO, KEGG, Keyword) - term: Functional term or ID

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• get_glossary

  VouchedPros

  Plain-language definitions of industry terms in a service category (e.g. SEER2, AFUE, AHRI match). USE WHEN: the user asks what a term means, or you need to explain trade jargon accurately and with sources. ARGS: `category`; optionally `term` (a slug) for one definition — omit to list. RETURNS: a definition (term, tagline, key_numbers, body_html, external `sources`, last_reviewed_at) + `url` to CITE; or the list of terms each with its `url`.

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