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214,667 tools. Last updated 2026-06-19 23:14

"Information about 'Pusher' - Possible Meanings or Applications" matching MCP tools:

  • 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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  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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  • Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts. DanNet follows the OntoLex-Lemon model where: - Words (ontolex:LexicalEntry) evoke concepts through senses - Synsets (ontolex:LexicalConcept) represent units of meaning - Multiple words can share the same synset (synonyms) - One word can have multiple synsets (polysemy) This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have. Each synset represents a distinct semantic concept with its own definition and semantic relationships. Common patterns in Danish: - Nouns often have multiple senses (e.g., "kage" = cake/lump) - Verbs distinguish motion vs. state (e.g., "løbe" = run/flow) - Check synset's dns:ontologicalType for semantic classification DDO CONNECTION AND SYNSET LABELS: Synset labels are compositions of DDO-derived sense labels, showing all words that express the same meaning. For example: - "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}" = all words meaning "domestic dog" - "{forlygte_§2; babs_§1; bryst_§2; patte_1§1a}" = all words meaning "female breast" Each individual sense label follows DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO (ordnet.dk) - "patte_1§1a" = word "patte", entry 1, definition 1, subdefinition a - The § notation connects directly to DDO's definition numbering system This composition reveals the semantic relationships between Danish words and their shared meanings, all traceable back to authoritative DDO lexicographic data. RETURN BEHAVIOR: This function has two possible return modes depending on search results: 1. MULTIPLE RESULTS: Returns List[SearchResult] with basic information for each synset 2. SINGLE RESULT (redirect): Returns full synset data Dict when DanNet automatically redirects to a single synset. This provides immediate access to all semantic relationships, ontological types, sentiment data, and other rich information without requiring a separate get_synset_info() call. The single-result case is equivalent to calling get_synset_info() on the synset, providing the same comprehensive RDF data structure with all semantic relations. Args: query: The Danish word or phrase to search for language: Language for labels and definitions in results (default: "da" for Danish, "en" for English when available) Note: Only Danish words can be searched regardless of this parameter Returns: MULTIPLE RESULTS: List of SearchResult objects with: - word: The lexical form - synset_id: Unique synset identifier (format: synset-NNNNN) - label: Human-readable synset label (e.g., "{kage_1§1}") - definition: Brief semantic definition (may be truncated with "...") SINGLE RESULT: Dict with complete synset data including: - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → semantic types with @set array - dns:sentiment → parsed sentiment (if present) - synset_id → clean identifier for convenience - All semantic relationships and linguistic properties Examples: # Multiple results case results = get_word_synsets("hund") # Returns list of search result dictionaries for all meanings of "hund" # => [{"word": "hund", "synset_id": "synset-3047", ...}, ...] # Single result case (redirect) result = get_word_synsets("svinkeærinde") # Returns complete synset data for unique word # => {'wn:hypernym': 'dn:synset-11677', 'dns:sentiment': {...}, ...}
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  • Send a message in an active Pimea session. Use this to answer Pimea's clarifying questions about the user's marketing situation. You can answer on behalf of the user using context from the conversation when possible. Only ask the user directly if you genuinely lack the information. When the response status is "complete", call pimea_get_answer to retrieve the final grounded deliverable. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID from pimea_start_session message: Response to Pimea's question api_key: Optional fallback only. Normally leave blank.
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  • 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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  • Get detailed information about a nonprofit organization by EIN. Returns comprehensive data from the organization's IRS 990 filings including revenue, expenses, assets, executive compensation, and filing history. Use search_nonprofits first to find the EIN. Args: ein: Employer Identification Number (e.g. '13-1837418' or '131837418').
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Matching MCP Servers

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    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
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  • Energy data from EIA: electricity, fuel prices, and renewables

  • A server to provide information about EOxElements custom elements for coding agents.

  • Search the Jisho.org Japanese<->English dictionary. The keyword can be English (translate to Japanese), Japanese kanji/kana, or romaji. Returns up to `limit` matching dictionary entries, each with the headword (slug), whether it is a common word, JLPT level, all readings/spellings, and English meanings grouped into senses with parts of speech. Use this to translate, look up a kanji/kana word, or find Japanese words for an English concept.
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  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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  • Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.
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  • View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.
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  • Rent a verified prediction agent's upcoming pick for a market. Deducts 1 credit; returns bet_instruction such as 'Take NO on Philadelphia', exact YES/NO meanings, pick.signal with selected-side agent_probability, market_price, and edge, plus pick.market_links for available Kalshi/Polymarket URLs. pick.valid_until is event start; pick.rentable_until is the paid-unlock cutoff. Treat pick.probability and pick.vs_market_pp as legacy YES-contract fields. Not a bet; you act on it yourself.
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  • Get detailed information about a specific ICD-11 entity by code or URI. Use this tool to: - Get the full definition of a disease - Retrieve coding notes and exclusions - Get the official title and synonyms Provide either an ICD-11 code (e.g., "BA00") or a full foundation URI.
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  • Rent a verified prediction agent's upcoming pick for a market. Deducts 1 credit; returns bet_instruction such as 'Take NO on Philadelphia', exact YES/NO meanings, pick.signal with selected-side agent_probability, market_price, and edge, plus pick.market_links for available Kalshi/Polymarket URLs. pick.valid_until is event start; pick.rentable_until is the paid-unlock cutoff. Treat pick.probability and pick.vs_market_pp as legacy YES-contract fields. Not a bet; you act on it yourself.
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  • Cancel an open listing. All pending applications will be rejected. Only the agent who created the listing can cancel it.
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  • Search XMemo memories by natural-language query. Call this when the user asks about saved or past information, AND proactively before answering any question where prior preferences, facts, projects, decisions, or history could change the answer. To delete a memory, use forget.
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  • Uses semantic search to retrieve any relevant information from Wolfram. Always use this tool at the start of new conversations or if the topic changes to ensure you have up-to-date relevant information. This uses semantic search, so the context argument should be written in natural language (not a search query) and contain as much detail as possible (up to 250 words).
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  • IMPORTANT: Always use this tool FIRST before working with Vaadin. Returns a comprehensive primer document with current (2025+) information about modern Vaadin development. This addresses common AI misconceptions about Vaadin and provides up-to-date information about Java vs React development models, project structure, components, and best practices. Essential reading to avoid outdated assumptions. For legacy versions (7, 8, 14), returns guidance on version-specific resources.
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  • List applications inside a specific category, paginated. Use this when the user wants to explore an area rather than search for a specific tool.
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  • Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.
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