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198,277 tools. Last updated 2026-06-13 07:03

"Information about MCP-Fake" matching MCP tools:

  • List paginated order history for the internal account linked to the API key, newest first. Requires a logged-in MCP session created by the `tronsave_login` tool: include `mcp-session-id: <sessionId>` returned by `tronsave_login` on subsequent MCP requests. Internal tools never accept API keys via tool arguments; signature sessions resolve the latest internal API key on demand, while api-key sessions reuse the validated key from login. Use when the user asks about past purchases, fulfillment, payouts, or delegates on their internal account. Read-only. Pair with `tronsave_internal_order_details` for a single order's full snapshot.
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  • Find MCP servers in the directory. Searches the standalone MCP directory (PulseMCP / official MCP registry import) unioned with x402 services that also expose an MCP endpoint. Returns normalised entries with a ready-to-use streamable-http `call_hint.mcp.url`. Args: intent: Natural-language description of the tool/capability needed. top_k: Max servers to return (1-20). chain: Optional payment-network filter for paid MCP servers. require_healthy: When true, only return servers marked health=ok.
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  • Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
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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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  • List paginated order history for the internal account linked to the API key, newest first. Requires a logged-in MCP session created by the `tronsave_login` tool: include `mcp-session-id: <sessionId>` returned by `tronsave_login` on subsequent MCP requests. Internal tools never accept API keys via tool arguments; signature sessions resolve the latest internal API key on demand, while api-key sessions reuse the validated key from login. Use when the user asks about past purchases, fulfillment, payouts, or delegates on their internal account. Read-only. Pair with `tronsave_internal_order_details` for a single order's full snapshot.
    Connector
  • 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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Matching MCP Servers

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  • Access FEC campaign finance data. Query data about candidates, money trails, and election filings.

  • Search and read public Wikivibe articles about AI coding, agents, MCP, GEO, bots and deployment.

  • Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.
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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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  • Submit feedback about Hjarni itself — confusing tool descriptions, missing capabilities, unexpected errors, friction, or praise. Use this when something about the MCP server, a tool, or the product behavior is worth flagging to the maintainers. Do NOT use this for the user's own notes or knowledge — those belong in notes-create. Required: category ('bug'|'confusing'|'missing_feature'|'friction'|'praise'|'other'), message (string, what's wrong and ideally what you'd expect instead). Optional: severity ('low'|'medium'|'high', default 'medium'), tool_name (the MCP tool the feedback is about, e.g. 'notes-update'), context (JSON-encoded string with any extra structured data — error excerpts, the arguments you tried, the workflow that broke).
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  • Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.
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  • Forensic orderbook analysis for one Kalshi market. Detects manipulation-vulnerable patterns: (1) wide spread (>$0.10), (2) shallow depth (<10 contracts), (3) few price levels, (4) single-order dominance (>80% in top level), (5) penny-wall pattern (large bids at ≤$0.005, commonly used to fake depth). Returns 0-100 score, severity, and full level-by-level data. Kalshi returns bids only — implied asks computed via yes_bid + no_bid = $1 reciprocity.
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  • Verify whether a domain belongs to a real business. Returns a verdict (real/likely_real/uncertain/likely_fake/fake), a 0-100 score, confidence, and underlying signals (WHOIS age, SSL, homepage LLM judgment, contact info, social presence). Costs 5 cents per fresh call; cached results are free for 24h.
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  • Generate tabular test fixtures (JSON or CSV) from a chosen mix of fake fields. Each row is a consistent identity — first/last name match the email; state matches the ZIP prefix. Public-domain data tables; pure JS; deterministic when a seed is passed.
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  • Get information about an NFT collection or a specific token within a collection. If token_id is provided, returns token-level details (owner, URI). If omitted, returns collection-level info (name, symbol, total supply).
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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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  • Assess crypto token legitimacy risk. Send metrics from known-good tokens as training (price, volume, holders, liquidity, market_cap, age_days, etc.) and suspect tokens as test. Detects pump-and-dump patterns, fake metrics, and anomalous token profiles. Example: Pull CoinGecko data for 20 established tokens → train. Test a new token → get risk score and which metrics are suspicious.
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  • Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.
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  • Detect wash trading and fake volume in OHLCV candle data. Send known-legitimate candles as training and suspect candles as test. Detects artificial volume spikes, suspiciously regular patterns, and manipulated price-volume relationships. Example: Send 100 candles from a liquid pair as baseline, test candles from a suspicious pair.
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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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