"WP Engine" matching MCP tools:

• list_signalsA

  mcpA

  [RAW FEED — engine inputs, NOT trade calls] List active n0brains signals with optional filters. Filters: asset (e.g. 'ETH'), signal_type (whale|sentiment|listing|regulatory|macro|macro_pulse|liquidation|funding|hack|price|other), direction (bullish|bearish|neutral), urgency (high|medium|low), min_confidence, min_score, limit (1-100, default 20), offset. Each signal includes historical_edge, paired_inverse, signal_latency_secs, priced_in_*, calibration_inverted_in_cell. CONFIDENCE CONTRACT: confidence = calibrated empirical win-probability estimate (binned per signal_type), NOT raw model output; when confidence is null, confidence_v2 + outcome_record (2026-09-02) = this asset x type x direction x context's measured 24h outcome record with a day-clustered interval, peer rank and `distinguishable` — quote it only with its n/days/ci95; confidence_suppressed_reason says why; confidence_status is one of calibrated|floor_demoted_at_emit|suppressed_anti_predictive|demoted_anti_predictive_type. Transform emitters (whale_position leaderboard fade) carry observed_direction/observed_behavior/model_transform/predicted_direction so the raw observation is never lost. Most rows carry action_hint=ignore — engine inputs, not calls; read historical_edge (cell win_rate) before echoing any direction. For tradeable output use get_actionable_signals.

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• read_fileA

  BorealHostB

  Read the contents of a file from a site's container. Max file size: 512KB. Binary files are rejected — use the site's file manager or SSH for binary files. Requires: API key with read scope. Args: slug: Site identifier path: Relative path to the file Returns: {"path": "wp-config.php", "content": "<?php ...", "size": 1234, "encoding": "utf-8"} Errors: NOT_FOUND: File doesn't exist VALIDATION_ERROR: File is binary or exceeds 512KB

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• list_signalsA

  n0brainsA

  [RAW FEED — engine inputs, NOT trade calls] List active n0brains signals with optional filters. Filters: asset (e.g. 'ETH'), signal_type (whale|sentiment|listing|regulatory|macro|macro_pulse|liquidation|funding|hack|price|other), direction (bullish|bearish|neutral), urgency (high|medium|low), min_confidence, min_score, limit (1-100, default 20), offset. Each signal includes historical_edge, paired_inverse, signal_latency_secs, priced_in_*, calibration_inverted_in_cell. CONFIDENCE CONTRACT: confidence = calibrated empirical win-probability estimate (binned per signal_type), NOT raw model output; when confidence is null, confidence_v2 + outcome_record (2026-09-02) = this asset x type x direction x context's measured 24h outcome record with a day-clustered interval, peer rank and `distinguishable` — quote it only with its n/days/ci95; confidence_suppressed_reason says why; confidence_status is one of calibrated|floor_demoted_at_emit|suppressed_anti_predictive|demoted_anti_predictive_type. Transform emitters (whale_position leaderboard fade) carry observed_direction/observed_behavior/model_transform/predicted_direction so the raw observation is never lost. Most rows carry action_hint=ignore — engine inputs, not calls; read historical_edge (cell win_rate) before echoing any direction. For tradeable output use get_actionable_signals.

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• update_template_draftA

  FaldaroA

  Save a template’s draft. Both kinds take structured `content` — see create_document_template for the node format; notifications use its EMAIL profile (no image, no pageBreak) — or, for a template still on the legacy HTML engine (check get_template_draft’s `engine`), a `body`; a structured draft refuses body-only writes. Notifications also take subject and recipients; documents also take `settings`. Editing a live template’s draft does not change what sends until publish_template. A document on the SVG facsimile engine (engine "SVG") is edited with edit_svg_template, never here. Requires notification:write or document:write.

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  Destructive

  API key

• get_arco_variablesA

  gdex-mcpA

  Return the list of ARCO variables available for a dataset. Each row's type (index 2) is either "zarr" or "reference" (kerchunk). A "reference" variable is often listed multiple times, once per kerchunk reference variant (plain, "-remote-https", "-remote-osdf", etc.) — always pick the "-osdf" variant's URL; the others' chunk targets can be internal paths that only resolve on NCAR's network. Open it with xr.open_dataset(url, engine="kerchunk", storage_options={"remote_protocol": "https", "lazy": True}) — that's better behaved than hand-building an fsspec reference filesystem. A "zarr" row needs no variant picking — its URL already routes through the OSDF director (rewritten automatically); open it directly with xr.open_dataset(url, engine="zarr"). Results are capped at 300 rows (look for "truncated": true); narrow with search_arco_variables if you hit the cap. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002

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• sixta_explain_errorA

  sixta-connectA

  Decode a database error and get the fix and the next step — no connection needed. Paste a MySQL error number (1213, 1062, 1452, 1205…) or a PostgreSQL SQLSTATE (40P01, 23505, 53300…), optionally with the failing statement, and get the proximate cause, the concrete fix, and — when it helps — the SIXTA tool and artifact to go deeper (e.g. a deadlock → paste SHOW ENGINE INNODB STATUS for sixta_explain_deadlock). Use when the user pastes a DB error code or message. Input is analyzed in memory and never stored.

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• formation.guided_setupA

  Admin SubstituteA

  RECOMMENDED ENTRY POINT — Intelligent business formation engine. Provide context about the person (trade, state, qualifications, budget, goals) and get a personalised formation plan with: (1) auto-detected pathway (licensed contractor, fresh start, or investor/operator), (2) task list split into AI-actionable vs human-required, (3) direct government portal links, (4) trade-specific insights (common first jobs, suppliers, rates, growth tips), (5) recommended next tool calls, (6) follow-up questions to ask. Call this FIRST, then use the recommended tool chain for deeper dives.

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• find_alphaA

  Name Whisper — ENS Intelligence LayerA

  Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six. It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of <collection>" — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.

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• engine_infoA

  Backtest360A

  Engine version, API contract number, and health. Free (not quota-counted). Call once at the start of a session to confirm the engine is reachable and which contract it serves.

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• list_indicatorsA

  Backtest360A

  List indicators, or fetch one indicator's full schema. Cheap, cacheable per session. With no arguments: a compact catalog — ``{"indicators": [...], "count": N}`` — where each entry carries id, name, category, kind, and value_dtype (no description, to keep the discovery scan small). Use it to discover what exists. Pass name='rsi' (id or name, case-insensitive) to get that single indicator's complete entry including its description and params_schema — do this before adding an indicator to a strategy so its parameters are exactly right. Pass compact=False for full entries for everything (large; the MCP server may cap it and set ``truncated_by_mcp`` — prefer compact or name=). Wire optimization: the compact discovery path asks the engine to omit per-entry descriptions (``descriptions=false``) since they are stripped locally anyway; the name= and compact=False paths request them. This is a pure saving — if the engine ignores the param it returns full entries and the local compact strip still yields a lean result.

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• run_backtestA

  Backtest360A

  Run a historical backtest against the engine. Quota-counted and compute-bound. Validate the strategy first (validate_strategy is far cheaper). On a 504 compute timeout, do NOT retry the same request — reduce the date range, use a coarser frequency, or simplify the strategy. On 429/503, wait for the advertised Retry-After before retrying. Args: data_source: Either inline OHLCV ({"ohlcv": {dates, open, high, low, close, volume?}} as parallel arrays, ISO-8601 dates) or a server-side fetch ({"symbol", "start", "end", "frequency"} — requires a paid plan). strategy: Strategy document (indicators[] + condition_tree). Mutually exclusive with signals. signals: Precomputed signal series ({"dates": [...], "values": [-1|0|1, ...]}). Mutually exclusive with strategy. execution: Execution/cost/risk/sizing settings. Use values from get_catalog('execution-modes'/'stop-types'/'sizing-methods'); omit for engine defaults. benchmark: Optional benchmark data source (same shape as data_source) — when given, the result also carries benchmark-relative metrics (beta, alpha, information ratio, tracking error, up/down capture) and bar-alignment info. data_inputs: Optional custom time-series the strategy references (name -> {dates, values}). response_detail: 'summary' (default — headline metrics, smallest), 'stats' (every metric), 'full' (plus trades and series downsampled to a fixed, server-controlled number of points). include: Optional add-on blocks at any detail level: 'trades', 'equity_curve', 'monthly_returns', 'yearly_returns', 'signal_diagnostics' (which per-bar entry/exit conditions fired, as capped fire-date lists — {"available": false, ...} if the run has none, e.g. precomputed signals). trades_limit: Max trades returned when trades are included. Returns: The shaped result at the requested detail (including ``benchmark_relative``/``alignment`` when a benchmark was given); an oversized result is thinned and marked ``truncated_by_mcp``. If the engine rejects the request as invalid (400/422), returns {"accepted": false, "error": ...} so you can fix the named field(s) and retry. Capacity, timeout, and permission failures (e.g. 429/503/504/401/403) raise a tool error carrying explicit recovery guidance.

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• get_muhurta_timingsA

  Kundlit Vedic AstrologyA

  Returns every auspicious and inauspicious time window for one date at one place, in a single call: all 16 choghadiya with quality, the 24 planetary horas, rahu kaal / yamaganda / gulika kala, abhijit + brahma + vijaya + godhuli + nishita muhurta and the two sandhyas, amrita kalam, varjyam, dur muhurtam, bhadra windows, panchak and gand mool status, and disha shool (the travel direction to avoid that weekday). Use this to CHOOSE a moment -- 'best time to sign / travel / start work today'. Use get_panchang instead when the user only wants the day's tithi and nakshatra; use get_pancha_pakshi when the timing must be personalised from full birth details. Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa); no writes, no auth, at least 30 requests/min/IP per server instance, plus a shared engine budget of at least 60/min/IP across all engine-backed tools. Windows are divisions of that location's actual sunrise-to-sunset day. If a window cannot be computed for the date the result names it under data_note as unknown rather than dropping it -- do not read absence as 'none today'.

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• get_year_eventsA

  Kundlit Vedic AstrologyA

  Returns dated Hindu calendar events for a whole year at one city. category='festival' lists every festival and vrat that year (Holi, Diwali, Navratri, Shivratri and the rest); the other categories list every ekadashi, purnima, amavasya or sankranti date. Each row is the exact local date and the event name, plus a one-line significance on shorter lists. Use this for 'when is X this year' and 'list all ekadashi dates' questions. For the detail of a single day (tithi, sunrise, rahu kaal) call get_panchang with that date; for eclipses call get_eclipses. Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa), served from a shared cache -- a cold or expired entry recomputes and can take ~30s; no writes, no auth, at least 30 requests/min/IP per server instance, plus a shared engine budget of at least 60/min/IP across all engine-backed tools. Constraints: the year must be within two years either side of the current year, and results are computed for the nearest of 50 supported cities (a location_note names it when the snap is far); long lists are capped at 80 rows, so pass month to page through them.

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• get_eclipsesA

  Kundlit Vedic AstrologyA

  Returns every solar and lunar eclipse of a calendar year with local visibility for one city: eclipse type (total, partial, annular or penumbral), date, the sparsha (first contact), madhya (maximum) and moksha (release) times, the magnitude, and a locally_visible flag. That flag is the one that matters for practice -- sutak observance applies only where the eclipse is actually visible. Use this for 'which eclipses fall in this year' and 'is it visible here' questions, and for sutak timing. For festivals and vrat dates use get_year_events; for the rest of a day's almanac use get_panchang. Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa), served from a shared cache -- a cold or expired entry recomputes and can take ~30s; no writes, no auth, at least 30 requests/min/IP per server instance, plus a shared engine budget of at least 60/min/IP across all engine-backed tools. Constraints: the year must be within two years either side of the current year, and visibility is evaluated for the nearest of 50 supported cities.

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• get_upapada_arudhaA

  Kundlit Vedic AstrologyA

  Returns the Jaimini layer of one birth chart: the upapada lagna (UL, the classical marriage anchor) with its sign, lord and cited reading, all twelve arudha padas (A1-A12) with sign and life domain, the eight charakarakas (Atmakaraka, Amatyakaraka and the rest) with the graha holding each, and the karakamsha lagna. The result also lists the classical texts the upapada reading is cited from. Use this only when Jaimini terms are asked for by name -- upapada, arudha, charakaraka, karakamsha -- or for marriage questions read the Jaimini way. The Parashari layer (grahas, houses, dasha, yogas) is in get_janam_kundali; get_kundli_match is the tool for scoring two charts against each other. Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa); no writes, no auth. Rate limits are counted per server instance: at least 30 requests/min/IP on this endpoint, plus a shared engine budget of at least 60/min/IP across all birth-chart tools. If the engine cannot attach the Jaimini layer the result comes back with partial: true rather than with invented values.

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• create_guidesA

  serpmanticsA

  Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.

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• get_market_briefA

  mcpA

  [START HERE — market overview] One-call morning brief: market regime (risk appetite), liquidity read, high-impact events next 72h, the engine's actionable reads (has_trade_signal + reads[]), and cross-asset trade-plan ranking — compact projections of get_market_regime / get_liquidity_map / get_economic_calendar / get_actionable_signals / rank_trades, assembled server-side. Optional coin arg scopes the actionable reads to that coin and adds it to the ranked set (ranking stays setup_score-sorted). Drill into any block with the underlying tool. Descriptive + engine verdicts; uncalibrated blocks labeled; not financial advice.

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• get_market_briefA

  n0brainsA

  [START HERE — market overview] One-call morning brief: market regime (risk appetite), liquidity read, high-impact events next 72h, the engine's actionable reads (has_trade_signal + reads[]), and cross-asset trade-plan ranking — compact projections of get_market_regime / get_liquidity_map / get_economic_calendar / get_actionable_signals / rank_trades, assembled server-side. Optional coin arg scopes the actionable reads to that coin and adds it to the ranked set (ranking stays setup_score-sorted). Drill into any block with the underlying tool. Descriptive + engine verdicts; uncalibrated blocks labeled; not financial advice.

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• verify_repayment_historyA

  droplinkedA

  Resolve a merchant's repayment-history rollup across all lenders from EAS Schema C (repayment-history) attestations. Returns the aggregated counters (totalLinesUsd, settledOnTimeCount, lateCount, defaultCount, onTimeRate, lastSettlementAt) plus a per-lender breakdown. Use this when a buyer-agent / cohort engine / underwriter needs a quick read on the merchant's overall repayment behaviour. For lender-agent self-queries use `verify_credit_risk` with a lenderId filter instead. Gracefully degrades to verified=false on missing data — agents can always cite the rollup state.

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• get_visibility_indexA

  dabyte.aiA

  The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 20 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_visibility_indexA

  DABLOCK AI Visibility IndexA

  The whole current release in one call: every tracked brand in crypto/Web3 with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 24 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers crypto/Web3 only; the sibling index at dabyte.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dablock.ai when quoting a number.

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