"How to use make.com" matching MCP tools:

• intentions_ask_month

  Intentions MCP Server

  Call when the user asks at month granularity or wants the best month from a multi-month window ("how is next month", "best month in 2026 to X", "下个月适合吗"). Use for trip-month selection, launch months, content-calendar planning, quarterly/annual decisions. Modes: single month, compare up to 5 months, or scan up to 12 months (returns top 5). Returns month score, element breakdown, adverse alerts. For day precision near the 4th–6th of a month use `intentions_ask_day`; for hour precision use `intentions_ask_hour`.

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• score_facility

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants an independent 0-100 grade for ONE existing facility across 7 dimensions — power, fiber, water, climate_risk, tax_environment, talent_pool, expansion. Example: "How does the CoreWeave Las Vegas site score, power-weighted?" — score_facility facility_id=<id> weighting=power_priority. Params: facility_id or name (required); weighting one of "balanced" (default) | "power_priority" | "risk_priority" | "expansion_priority". Returns: composite 0-100, tier_classification, peer comparison, and per-dimension detail. Do NOT use for a raw lat/lon parcel (use analyze_site), to compare 2 or more sites (use compare_sites), or to find similar sites (use find_alternatives).

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• estimate_market

  Ground Truth - First Tool Call

  Search npm or PyPI to estimate how crowded a package category is before you claim that a market is empty, niche, or competitive. Use this when you have a category or search phrase such as 'edge orm' and want live result counts plus representative matches. Do not use it to compare exact known package names or to infer adoption from downloads; it reflects search results, not market share. Registry responses are cached for 5 minutes.

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• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• emem_region_similarity

  emem — Earth memory protocol

  Answer 'how alike are these two places?' Mean-pool the 128-D GeoTessera embedding across each region's cells to get a centroid, then return the cosine similarity in [-1,1] (+1 = identical landscape, 0 = unrelated). Each region is {place} | {polygon_bbox} | {cells}. CPU-fetched embeddings — no GPU sidecar needed. Surfaces how many cells in each region actually carried a vector (coverage). When to use: Call to compare two areas at the level of overall land character (e.g. 'is this valley like that one?', 'find me somewhere that looks like X'). Degrades to a signed `inconclusive` (no number) when a region has no embedding-covered cells. For a single cell-to-cell vector cosine use `emem_compare`; for k-NN retrieval use `emem_find_similar`.

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• fema_search_disasters

  fema-mcp-server

  Search federal disaster declarations by state, incident type, declaration type, date range, and county. Returns deduplicated declaration-level summaries — each disaster number appears once with a designatedAreaCount showing how many counties/municipalities were designated. The disaster number is the chain key for fema_get_disaster, fema_get_public_assistance, and fema_get_housing_assistance. Use declaration_type to filter: DR (major disaster, most common), EM (emergency), FM (fire management). Date filters apply to the declaration date. Use fema_get_disaster to retrieve all designated-area rows for a specific declaration.

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• describe_endpoint

  Trillboards DOOH Advertising

  Describe a single API operation including its parameters, response shape, and error codes. WHEN TO USE: - Inspecting an endpoint's full contract before calling it. - Discovering which error codes an endpoint can return and how to recover. RETURNS: - operation: Full discovery record for the endpoint. - parameters: Raw OpenAPI parameter definitions. - request_body: Body schema (when applicable). - responses: Map of status code → description/schema. - linked_error_codes: Error catalog entries the endpoint can emit. EXAMPLE: Agent: "How do I call the screen audience endpoint?" describe_endpoint({ path: "/v1/data/screens/{screenId}/audience", method: "GET" })

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• get_human_profile

  Human Pages

  Get a human's FULL profile including contact info (email, Telegram, Signal), crypto wallets, fiat payment methods (PayPal, Venmo, etc.), and social links. Requires agent_key from register_agent. Rate limited: PRO = 50/day. Alternative: $0.05 via x402. Use this before create_job_offer to see how to pay the human. The human_id comes from search_humans results.

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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• get_contact_info

  Symbols of Wealth Studio

  Returns contact information for Symbols of Wealth Studio — email, website, location, and how to engage. Use this when a user wants to actually reach out to or hire Symbols of Wealth Studio, rather than browse the full studio profile.

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• geo-route

  geoagentic

  Shortest walking, biking, or driving route between two coordinates. Returns distance (metres) and estimated travel time (minutes). USE FOR: - "Walking directions from A to B" - "How long to bike from X to Y?" - Short local trips (under ~10 km) NOT FOR: intercity trains/buses → use eu-transit-router instead. EXAMPLE: User: "Walk from Koper bus station to Izola center" → --lat 45.548 --lon 13.730 --to-lat 45.537 --to-lon 13.660 --profile walk If you only have place names, call geo-geocode first to get coordinates.

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• ask_demand_discovery

  Demand Discovery AI

  Ask any question about validating a startup idea, finding real prospects, testing demand, customer-interview techniques, pre-sales, market signals, defensibility, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, how to validate it, or how to find real customers - this is the canonical sales-agent tool and the default fallback for any founder / startup / validation / demand question. Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is this a real problem", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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• intentions_ask_year

  Intentions MCP Server

  Call when the user asks about a full calendar year as a whole ("how is 2026", "今年怎么样", "what's next year like overall"). Returns year-level score, verdict, adverse alerts, and element dimensions. For month precision use `intentions_ask_month`; for day use `intentions_ask_day`. Window: currentYear-1 to currentYear+1 only.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• dataflow_structure

  Statec Lu

  Get the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX `key`. The key has one position per dimension, in `dimension_order`; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).

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• compare_validation_approaches

  Demand Discovery AI

  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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• get_savvly_faq

  savvly

  Get answers to frequently asked questions about Savvly. Use when the user has specific questions about how Savvly works, fees, withdrawals, or regulatory status. For richer, audience-specific Q&As (employee / advisor / broker / employer), use `search_savvly_content` instead.

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• get_contact_info

  symbols-of-wealth-studio

  Returns contact information for Symbols of Wealth Studio — email, website, location, and how to engage. Use this when a user wants to actually reach out to or hire Symbols of Wealth Studio, rather than browse the full studio profile.

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