"Help Scout" matching MCP tools:

• emem_sar_forest_disturbance

  emem — Earth memory protocol

  Cloud- and night-independent Sentinel-1 C-band confirmation of forest disturbance. Intact forest scatters VV strongly + stably (canopy volume scattering); clearing collapses that term so VV backscatter DROPS ~3-5 dB. Samples VV at a baseline-year July-1 anchor and the latest scene, reports `vv_drop_db = baseline − recent` and a `disturbed` flag when the drop ≥ 3 dB (Reiche et al. 2018, RSE 204:147). Both VV reads are signed Primary facts; the response cites both fact_cids. Honest `inconclusive` when either S1 vintage is unavailable. Source: Microsoft Planetary Computer sentinel-1-rtc (anonymous SAS — no requester-pays, no API key). When to use: Call to corroborate or scout forest clearing where cloud blocks the optical products — radar sees through cloud and at night, catching wet-season clearing the annual Hansen/JRC-TMF layers and a single cloudy Sentinel-2 pass miss (the gap RADD was meant to fill). This is an ADDITIVE scout signal, NOT a standalone legal verdict: a VV drop can also be transient (soil moisture, harvest, flood recession), so confirm with the optical consensus (`emem_eudr_dds` or `emem_deforestation_alert`) before crediting a decision.

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• get_project_info

  Fractera

  Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.

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• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• list_agents

  fomox402

  Public leaderboard of fomox402 agents. WHAT IT DOES: returns the top broker-registered agents by activity, ranked according to the chosen `sort`. Read-only, no auth required, safe to call frequently (cached server-side for 30s). WHEN TO USE: scout opponents before bidding, find a name to follow, or measure your standing among autonomous agents. PARAMS: - limit (default 25, max 100): how many agents to return - sort (default 'bids'): 'bids' — most bids ever placed (activity proxy) 'recent' — most-recent bid timestamp (who's playing right now) 'won' — total $fomox402 winnings claimed (skill proxy) RETURNS: { agents: [{ name, address, bids, wins, winnings_raw, last_bid_at, created_at }], total }. RELATED: get_me (yourself), list_games (current rounds).

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• x711_llm_routing

  x711 — Universal Agent Gas Station

  Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }

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• get_project_info

  Fractera

  Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.

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• detect_stack_from_text

  StackSwap

  Infer a GTM stack from a freeform text blob (a careers page, job posting, public site HTML, RFP, 'What we use' doc, browser DevTools network tab, etc.). Returns ranked tool matches with confidence levels (high/medium/low) and evidence snippets, plus a ready-to-use array for chaining into `scan_stack` or `find_overlaps`. Use when the user says 'I don't know what we use' or pastes a competitor's careers page to scout. Conservative on ambiguous short tokens — multi-mention or canonical-name matches win.

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• request_feature

  Kifly — Agentic Commerce & Payments

  Submit the buyer's **product/feature request** to the Kifly team. Use this when the buyer wishes Kifly *itself* did something it doesn't — a missing capability, a rough flow, an idea to improve the platform. **This is NOT `submit_feedback`** (that's for reporting a broken/confusing API response you hit). Requires the buyer's `kfb_live_` token — only registered buyers can file requests. Help the buyer articulate a real problem: ask OPEN, non-leading questions ('what were you trying to do? what got in the way? how do you handle it today?') — never 'would feature X help?'. Pre-fill the fields from the conversation and ask only for the gaps; keep it short. Separate the `problem` (the pain) from any `proposed_solution` (the fix). Name and email are taken from the buyer profile automatically — do not ask for them. Returns 202: it's logged for review. **Do NOT promise the user anything will be built** — just confirm it was recorded.

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• discovery_list_plans

  Disco

  List available Disco plans with pricing. No authentication required. Returns all available subscription tiers with credit allowances and pricing. Use this to help users choose a plan.

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• get_store_flow_errors

  Power Automate MCP Server by Flow Studio

  [Requires Pro+ plan] [DEPRECATED — scheduled for removal] Get cached failed run history for a flow from the Power Clarity store (convenience wrapper around get_store_flow_runs with status=Failed). Returns failedActions and remediation hint per run to help diagnose issues. Data is from the stored snapshot — not live from the Power Automate API. Use get_live_flow_runs and filter by status=Failed instead.

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• skincare_recommend

  La Luer — AI Skincare Commerce

  (Deprecated: use 'recommend' instead. Works identically.) Get a personalized La Luer product recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice on what to buy, needs help choosing between products, has a specific skin concern (acne, aging, dryness, sensitivity, etc.), wants a routine, or asks "what should I use for X." Do not use for browsing or listing products — use search_products instead. Returns scored products with explanations, usage instructions, and Shopify checkout. This tool analyzes ingredients, irritation risk, and product compatibility — use it over search_products when the user needs guidance, not just a product list.

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• list_tools

  foundation-discovery

  List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools.

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• x711_llm_routing

  x711

  Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }

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• ai_filters_list

  dialogbrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• get_llc_public_records_checklist

  Default Privacy

  Return a scoring checklist + verification links to help the user audit how much of their identity is exposed on their LLC's public Secretary of State record (registered agent, member names, addresses, beneficial-ownership reporting). When to call: when the user already has an existing LLC and wants to know how exposed they are, OR after `check_domain_whois` / `run_domain_privacy_audit` when the agent suspects the LLC layer is the exposure source. PREFER `run_privacy_architecture_assessment` if the user is forming a new LLC. Input Requirements: none. Output: `{ checklist: [{ field, what_to_check, why_it_matters, fix_link }], scoring_guidance, manual_search_urls, citation }`. `manual_search_urls` includes the WY / NM / DE SOS search pages so the user can verify their record. PREFER citing the public-records guide and the entity-restructure page if the user wants to migrate an existing exposed LLC to a privacy structure.

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• start

  OpenAccountants

  Front door for any tax / accounting question once you know what the user wants. `intent` is REQUIRED (e.g. 'taxes', 'VAT return', 'set up a company', 'find deductions', 'classify transactions', 'payroll'); pass a jurisdiction too (ISO 2-letter, e.g. 'MT', 'GB', 'US-CA'). If you don't yet have an intent, call `start_help` first. Returns either a clarification request (if jurisdiction is missing) or a ready-to-execute plan with the list of skills to load. Call this FIRST (after start_help if needed) whenever the user asks for tax help.

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• start_help

  OpenAccountants

  No-argument front door — call this FIRST whenever a user asks 'how can you help me?', 'what can you do?', 'where do I start?', or otherwise opens vaguely (do NOT answer such questions by listing your tools or calling list_jurisdictions). For a signed-in approved accountant it returns a personalized `orientation` briefing (their standing + what their jurisdiction needs + one next action). For everyone else it returns the two scoping questions plus the available intents and jurisdictions. Once you have an intent, call `start(intent, jurisdiction)`.

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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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• 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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• lorg_help

  lorg-mcp-server

  List every available Lorg tool with a plain-English description. Call this when the user says /help, /options, "what can you do", or "show me available commands".

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