"How to understand a Google Sheet" matching MCP tools:

• get_payment_options

  headless-oracle

  Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.

  Connector

• pentest_encode

  pentest-mcp-server

  Transform a payload string through one or more encoding layers for bypass research during authorized testing. Accepts a chain of encodings applied in order (e.g., ["unicode", "url", "base64"] applies Unicode → URL-encode → base64). Returns the transformed payload with a step-by-step decoding explanation: how a WAF or server would decode each layer, and why the combined encoding might bypass a specific filter. Use to understand filter bypass mechanics in an authorized engagement and to confirm that a target's decoding pipeline matches an expected bypass path. Payloads are transformed mathematically — no live probing occurs.

  Connector

• get_history

  Silicon Pantheon

  Read-only. Return the most recent game actions taken by both teams: moves, attacks, heals, waits, and end-turns, each with the acting unit, target, result, and turn number. last_n controls how many actions to return (default 10, max 100). Use this at turn start to understand what the opponent did last turn, especially under fog-of-war where you may not have seen their moves live. For aggregate match statistics use get_match_telemetry instead.

  Connector

• revit_get_sheets

  Revit MCP

  When to use: Enumerate the drawing sheets (title blocks with sheet number + sheet name like 'A-101: First Floor Plan') published from a translated Revit model, so an agent can pick which sheet to render, review, or cross-reference. When NOT to use: Do not use to list model views like floor plans or 3D views (use revit_get_views) — this returns only 2D sheet entries. APS scopes: data:read viewables:read (Model Derivative metadata + object tree). Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired — refresh. 403 scope insufficient — add viewables:read. 404 URN not found — check model_id. 429 rate limited — back off. 5xx APS upstream — retry with jitter. Side effects: Read-only. Idempotent.

  Connector

• analyze_balance_sheet

  HelloBooks AI Agents MCP Server

  Take a Balance Sheet CSV export from QuickBooks Online, Xero, Zoho Books, or Wave (source auto-detected) and run three checks: (1) bs.equation_broken — the fundamental accounting equation Assets = Liabilities + Equity does not hold (every downstream ratio analysis is invalid until fixed); (2) bs.negative_asset — Cash / AR / Inventory line items with negative balances (reconciliation error signal); (3) bs.negative_equity — Total Equity < 0 (insolvency signal). Input is raw CSV text of a Balance Sheet (Reports → Balance Sheet in QBO / Xero / Zoho / Wave). Max 5,000 rows; max 5 MB. Returns flags with severity, totals (totalAssets, totalLiabilities, totalEquity, equationBalances boolean), and a shareable URL. Use this when a user pastes a Balance Sheet and asks "does my balance sheet balance?", "is the accounting equation satisfied?", or "is my company solvent on paper?". A Balance Sheet that fails Assets = Liabilities + Equity invalidates every downstream financial-ratio analysis — this is the single most important check for any BS.

  Connector

• list_capabilities

  Axcess — Design Accessibility Evaluation

  Returns available evaluation tools, what they check, and their pricing. Call this first to understand what Axcess can evaluate and how much each evaluation costs. This tool is FREE. All evaluation tools require USDC payment on Base network. Returns: JSON with tool descriptions, pricing, and rubric categories.

  Connector

• get_related_terms

  Arco Lexicon

  Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.

  Connector

• show_defaults

  Unphurl

  Show all 25 scoring signals with their default weights and descriptions. This is the baseline scoring that applies when no custom profile is specified. Use this to understand what each signal means and how much it contributes to the score before creating custom profiles. Profiles are sparse overrides on top of these defaults. This tool does not require an API key. The defaults are hardcoded and always available.

  Connector

• faa_get_aircraft_type

  faa-aircraft-registry-mcp-server

  Decode a 7-character FAA manufacturer/model/series code to aircraft specifications from the reference table — manufacturer, model, aircraft category, aircraft type, engine type, number of engines, number of seats, weight class, cruise speed, and type-certificate data sheet/holder. Use faa_search_aircraft_types first to discover a code by manufacturer or model name.

  Connector

• maps_directions

  Google_maps

  "Google Maps directions from A to B" / "transit / public-transport directions" / "bus / subway / train route" / "best way to get from [X] to [Y]" — turn-by-turn directions via Google Maps. Modes: driving, walking, transit (bus/subway/train), bicycling. Requires Google Maps API key. PREFER over Mapbox/OpenRouteService specifically for public-transit routing — Google has the best transit data.

  Connector

• get_attributes_for_model

  OpenDataModels MCP server

  List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})

  Connector

• 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".

  Connector

• google_ads_list_accessible_customers

  paid-ads

  Lists directly accessible Google Ads customers for the configured Google Ads credentials, including descriptive names when Google returns them. Use this to discover customer IDs before running Google Ads hierarchy or reporting tools.

  Connector

• list_sheets

  Bilig WorkPaper

  Discover sheet names and used dimensions before reading or editing a WorkPaper. Returns metadata only; use read_range or read_cell for values.

  Connector

• maps_place_details

  Google_maps

  "Hours / phone / reviews of [business]" / "Google business info for [place]" / "is [restaurant] open" — full details for a Google Place: address, phone, hours, website, ratings, user reviews. Requires a place ID from `maps_place_search`. Use after search to drill into one specific business.

  Connector

• get_help

  mcp-server

  Searches the official Quanti documentation (docs.quanti.io) to answer questions about using the platform. **When to use this tool:** - When the user asks "how to do X in Quanti?", "what is a connector?", "how to configure BigQuery?" - When the user needs help configuring or using a connector (Google Ads, Meta, Piano, etc.) - To explain Quanti concepts: projects, connectors, prebuilds, data warehouse, tag tracker, transformations - When the user asks about the Quanti MCP (setup, overview, semantic layer) **This tool does NOT replace:** - get_schema_context: to get the actual BigQuery schema for a client project - list_prebuilds: to list pre-configured reports for a connector - get_use_cases: to find reusable analyses - execute_query: to execute SQL **Available topic filters:** connectors, data-warehouses, data-management, tag-tracker, mcp-server, transformations

  Connector

• list_rows

  Dock

  List rows in a workspace's table surface. Returns rows with their data (a JSON object of column-name to value), creation time, the principal who created/updated each row, AND the row's `surface_slug` (the sheet it lives on). Empty array if no rows have been added yet. Multi-surface workspaces: pass `surface_slug` to scope to one sheet; omit to return rows from every surface in the workspace (back-compat: pre-multi-surface clients keep working).

  Connector

• product_review_plan

  website-search

  Get Lenny Zeltser's expert criteria for reviewing an existing product strategy plan. Returns focused guidance for constructive critique—what to check in each section, strategic coherence issues, and how to frame feedback collaboratively. Includes rating-sheet items (the lens taxonomy: structure, words, tone) as concrete reference points for grounded feedback on the plan's writing. This server never requests your plan and instructs your AI to keep it local. Use market_segment: "smb" to include SMB-specific review criteria. Use product_focus: "endpoint" to include endpoint viability assessment.

  Connector

• ir_load_context

  website-search

  Load Lenny Zeltser's IR report writing context for local analysis. Returns expert guidelines for field completeness, incident identification, notification triggers, and writing quality. Includes rating-sheet items (lens taxonomy plus the IR-specific Information sheet) as concrete reference points for grounded feedback. This server never requests your incident notes and instructs your AI to keep them local. Use detail_level to control response size: "minimal" (~2k tokens), "standard" (~5k tokens), or "comprehensive" (~11k tokens).

  Connector

• google_ads_render_weekly_group_report

  paid-ads

  User-facing render tool for Google Ads visual weekly reports. Use this directly for prompts like 'show me a Google Ads report', 'generate a Google Ads dashboard', or 'show 7/30/90-day Google Ads performance'. Do not first call google_ads_get_weekly_group_report unless you already need raw data for a non-visual answer; when this visual report renders, keep any assistant text to a brief confirmation.

  Connector