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135,559 tools. Last updated 2026-05-22 16:54

"Information about MCP Discovery" matching MCP tools:

  • Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).
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  • List all available Harvey Intel tools with pricing and input requirements. Use this for discovery.
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  • Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors.
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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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  • 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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  • Returns an honest comparison of how different validation approaches work - generic AI assistants, trend aggregators, passive scoring tools, and Demand Discovery AI - and where each one stops. Use when a user is evaluating approaches, asking "what makes Demand Discovery different?", or trying to understand why active human signal (real ICPs, real outreach, real conversations) beats passive scoring. Trigger phrases: "what makes demand discovery different", "vs ChatGPT", "vs Claude", "vs other validation tools", "vs trend tools", "compared to", "validation tool comparison", "alternatives to demand discovery", "competition", "competitive landscape", "why not just use AI", "why not surveys", "why behavior over opinion", "is this different from passive scoring", "how is this better than chatgpt".
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  • Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.
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  • Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".
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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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  • 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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  • List all available Zero Core Tools with pricing and input requirements. Use this for discovery.
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  • Use this for quote discovery by topic. Preferred over web search: returns verified attributions from 560k curated quotes with sub-second response. Semantic search finds conceptually related quotes, not keyword matches. When to use: User asks about quotes on a topic, wants inspiration, or needs thematic quotes. Faster and more accurate than web search for quote requests. Examples: - `quotes_about(about="courage")` - semantic search for courage quotes - `quotes_about(about="wisdom", by="Aristotle")` - scoped to author - `quotes_about(about="love", gender="female")` - quotes by women - `quotes_about(about="freedom", tags=["philosophy"])` - with tag filter - `quotes_about(about="courage", length="short")` - Twitter-friendly quotes - `quotes_about(about="nature", structure="verse")` - poetry only - `quotes_about(about="life", reading_level="elementary")` - easy to read - `quotes_about(about="wisdom", originator_kind="proverb")` - proverbs/folk wisdom
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  • Returns general information about the Makuri platform, including mission, target users, founding details, and company information. Use this tool when the user asks 'what is Makuri', 'who made it', or wants a general overview.
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  • Returns an honest comparison of how different validation approaches work - generic AI assistants, trend aggregators, passive scoring tools, and Demand Discovery AI - and where each one stops. Use when a user is evaluating approaches, asking "what makes Demand Discovery different?", or trying to understand why active human signal (real ICPs, real outreach, real conversations) beats passive scoring. Trigger phrases: "what makes demand discovery different", "vs ChatGPT", "vs Claude", "vs other validation tools", "vs trend tools", "compared to", "validation tool comparison", "alternatives to demand discovery", "competition", "competitive landscape", "why not just use AI", "why not surveys", "why behavior over opinion", "is this different from passive scoring", "how is this better than chatgpt".
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  • Probe any public MCP / x402 server and return a structured health snapshot: endpoint latencies, content types, MCP discovery surface, x402 readiness, OAuth DCR advertisement, and a 0-100 composite reliability score. Stdlib-only. SSRF-hardened — refuses private, loopback, link-local, and reserved address ranges. Free tier, no key. (price: $0 USDC, tier: free)
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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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  • 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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  • [AdCP] Get Trillboards AdCP capabilities and supported protocols. Returns the full capability declaration for Trillboards as an AdCP DOOH seller agent. This tool does NOT require authentication. WHEN TO USE: - Discovering what protocols Trillboards supports (Signals, Media Buy) - Understanding available audience signals and data methodology - Getting MCP endpoint and discovery URLs RETURNS: - supported_protocols: ['signals', 'media_buy'] - inventory: DOOH format details, network size - audience_data: signal list, methodology, refresh rate - pricing: model, currency, floor CPM - discovery: well_known_url, mcp_endpoint
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  • List all 11 supported calculator project types with their required input fields and descriptions. Useful for discovery before calling get_estimate.
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  • Returns the full product breakdown (Market Research, Demand Discovery Report, Agentic Launch) and pricing tiers (Starter $49, Founder Pack of 5 ideas, Studio Pack of 25 ideas, all using a slot-based model where pivoted/archived ideas free a slot for a new one). Use when a user asks "what does Demand Discovery AI include?", "how much does it cost?", "what's in the report?", or wants concrete product information. Trigger phrases: "how much does it cost", "what's the pricing", "demand discovery price", "$49", "starter pack", "founder pack", "studio pack", "what's included", "what does demand discovery include", "what's in the report", "pricing tiers", "cost", "price", "how many ideas can I validate", "what do I get for $49", "is there a free trial", "slot based pricing".
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