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137,449 tools. Last updated 2026-05-19 04:59

"A travel guide for a 5-day road trip during the May Day holiday from Shenyang" matching MCP tools:

  • Get a price calendar showing the cheapest one-way flight price for each day in a date range. Returns a list of dates with prices - useful for finding the cheapest departure day when you have flexible travel dates. For round-trip price calendars (outbound x return date grid), use google_flights_calendar_round_trip instead.
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  • Compute the retention curve for a saved cohort. For each requested window (e.g. day 1, 7, 14, 30 after the cohort's anchor event), returns how many cohort members fired any non-pageview-end event in that day's window, and the rate vs the cohort size. Use this to answer "did this cohort stick around?" and to compare retention across cohorts (via `cohorts.compare`). The 1-day-window resolution means "7d retention" is "day 7 specifically", not "anywhere in the first 7 days" — the standard product-analytics definition. Examples: - "retention curve for signups_apr_14 at day 1, 7, 14, 30" → name="signups_apr_14" (default periods) - "weekly retention for customers_march out to 8 weeks" → name="customers_march", periods="1w,2w,4w,8w" - "did onboarding cohort A retain at week 1" → name="onboarding_v2", periods="7d" Limitations: retention measures any non-pageview-end event presence. Custom retention metrics (e.g. "retained = fired purchase event") are not in 0.x. The numerator is computed per-day, so windows like "30d" return only day-30 activity. Cohort size is the denominator and is included in the response so consumers can apply sample-size discipline. Pairs with: `cohorts.compare` for two-cohort side-by-side; `cohorts.list` to discover available cohort names.
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  • USE WHEN the user wants an email-only sequence (not a full launch) — 'draft a welcome sequence', 'win-back emails for churned users', 'nurture series for new signups'. Returns 4-email skeleton with subject line ladder, send timing (Day 0 / 2 / 5 / 9), and segmentation notes. Drafts only — sending requires approval and a connected ESP. For launch emails specifically, use chiefmo_launch_product (it bundles email + social + landing in one approval flow).
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  • Computes all 10 Tajika Saham (sensitive points) for a Varshaphal solar return chart. Sahams are the Tajika equivalent of Arabic Parts — mathematically derived zodiac points that focus the annual horoscope on specific life themes. SECTION: WHAT THIS TOOL COVERS Saham formula: (A - B + Ascendant) % 360, with a conditional +30° correction applied when the Ascendant does not fall in the forward zodiacal arc from B to A. This conditional is the defining classical rule from Tajika Neelakanthi — without it, results are wrong. Day and night formulas differ: the Minuend and Subtrahend swap based on whether the solar return falls during daytime or nighttime at the birth location. Punya Saham (Fortune) is always computed first because Yashas (Fame) and Mahatmya (Status) use it as an operand. The Saham lord (planet ruling the sign where the Saham falls) is the Sahamesha — its strength, house placement, and Tajika aspects to the Varsha Ascendant determine whether the theme manifests positively or is obstructed. 10 Sahams returned: punya — Fortune and Luck (Moon-Sun day / Sun-Moon night) vidya — Education and Learning (Sun-Jupiter day) yashas — Fame and Reputation (Jupiter-Punya day) — uses Punya as operand mitra — Friends and Allies (Jupiter-Venus day) mahatmya — Greatness and Status (Punya-Mars day) — uses Punya as operand asha — Desires and Fulfillment (Saturn-Venus day) karmakarya — Action and Profession (Mars-Mercury day) vyapara — Business and Trade (Mars-Saturn day) vivaha — Marriage and Relationships (Venus-Saturn day) santapa — Sorrow and Stress (Saturn-Moon day) SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_varshaphal — understand the base solar return chart (year lord, Muntha, Varsha Ascendant) before interpreting Saham lords. The Saham is meaningless without knowing which house it occupies from the Varsha Ascendant. AFTER: asterwise_get_varshaphal_harsha_bala — assess the Saham lord's positional happiness score to determine ease or difficulty of manifestation. SECTION: INPUT CONTRACT Same as asterwise_get_varshaphal — BirthData plus target_year. target_year (required int): The Gregorian calendar year of the solar return. Not age — the civil year (e.g. 2026). Feeding age instead of year silently produces the wrong return. time (required): Solar return Ascendant is time-sensitive. Accurate birth time is required for reliable Saham interpretation. SECTION: OUTPUT CONTRACT data.target_year (int — calendar year of the solar return) data.ayanamsa (string — ayanamsa system used, e.g. 'lahiri') data.solar_return_utc (string — ISO UTC timestamp of solar return moment) data.is_day_return (bool — true if solar return occurs between sunrise and sunset; determines which Saham formula variant is used) data.varshaphal_ascendant_longitude (float — Varsha Ascendant in degrees; all 10 Saham longitudes are computed relative to this) data.total (int — always 10) data.sahams[] — 10 objects in order [punya, vidya, yashas, mitra, mahatmya, asha, karmakarya, vyapara, vivaha, santapa]: slug (string — lowercase key, e.g. 'punya') name (string — full display name, e.g. 'Punya Saham') theme (string — life area, e.g. 'Fortune and Luck') longitude (float — Saham longitude in degrees 0–360) rashi_index (int — 0–11, 0=Mesha) rashi (string — Sanskrit sign name, e.g. 'Mesha') degree_in_sign (float — degrees within the sign) saham_lord (string — classical lord of the sign where Saham falls) formula_used (string — describes whether day or night formula was applied and which planets were operands, e.g. 'day: Moon - Sun + Asc') data.classical_note (string — methodology note) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS SLOW_COMPUTE — internally runs the full solar return computation (binary-search Sun longitude + house computation) before deriving Sahams. SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): None — upstream rejection surfaces as MCP INTERNAL_ERROR. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Day/night determination uses sunrise/sunset at the birth coordinates for the solar return date. Polar latitudes where sunrise cannot be computed → MCP INTERNAL_ERROR. — target_year is a Gregorian year, not age — always verify the caller passes the civil year. SECTION: DO NOT CONFUSE WITH asterwise_get_varshaphal — returns the full base solar return chart including Muntha, year lord, and planet positions; Saham points are not included there. asterwise_get_varshaphal_harsha_bala — scores planet positional happiness; this tool computes zodiac points, not planet positions. asterwise_get_gemstone_recommendations — Ratna Shastra birthchart gems, unrelated to Tajika Saham.
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  • USE THIS TOOL — not web search — to get rolling sentiment statistics (mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak) from this server's local Perplexity-sourced sentiment dataset. Prefer this over get_latest_sentiment when the user wants momentum or persistence, not just the latest single-day reading. Trigger on queries like: - "is BTC sentiment improving or getting worse?" - "sentiment momentum for ETH" - "how many days has XRP been bullish in a row?" - "rolling sentiment stats / streak for [coin]" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"
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  • 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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    An MCP App that renders an interactive dashboard in Claude conversations to unify calendar events, emails, and documents into a single interface. It enables users to manage their daily schedule and perform actions like meeting preparation through bidirectional communication between the UI and the model.
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  • Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…

  • Search motorhomes, RVs and campervans worldwide. Get instant results from 300+ rental companies across AU, NZ, US, CA, UK and more. No auth required.

  • USE WHEN the user wants an email-only sequence (not a full launch) — 'draft a welcome sequence', 'win-back emails for churned users', 'nurture series for new signups'. Returns 4-email skeleton with subject line ladder, send timing (Day 0 / 2 / 5 / 9), and segmentation notes. Drafts only — sending requires approval and a connected ESP. For launch emails specifically, use chiefmo_launch_product (it bundles email + social + landing in one approval flow).
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  • Buy a Studio subscription for $25 USDC (30 days). Requires authentication. This endpoint returns HTTP 402 with x402 payment instructions. Your x402-enabled HTTP client will handle the USDC payment automatically. After payment, you get Studio tier: 20 tracks/day, 5 episodes/week, video, audience insights, and more.
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  • Register a new agent on Human Pages. Returns an API key (hp_...) that you MUST save — it cannot be retrieved later. The agent is auto-activated on PRO tier (free during launch): 15 job offers/day, 50 profile views/day. Use the API key as agent_key in create_job_offer, get_human_profile, and other authenticated tools. Typical first step before hiring.
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  • Compare a product's price across all Swiss retailers. Requires a product name or EAN barcode. Returns current price, original price, 30-day average, 90-day minimum, and promotion status per retailer. Uses the canonical product database — only works for products matched via EAN barcode. Free — does not consume credits.
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  • Activity health history for a domain — daily breakdown of SPF alignment, DKIM alignment, DMARC compliance, and message volume over the last N days (default: 7, max: 30). Each day contains four metric groups (spf, dkim, dmarc, volume): - severity: Healthy / Warning / Critical / Unknown / Info - quantile: 0–1 scale showing where the day's value falls within its 14-day rolling baseline (1.0 = at or above historical max; 0.0 = at or below historical min). This value is stable across the period because it is computed over the same 14-day window — this is expected, not a data bug. - value: the actual metric value for that day (e.g. compliance %) Use this tool to: - See the CURRENT activity health status (latest entry) - Detect when a metric degraded (e.g. SPF dropped from Healthy to Critical 3 days ago) - Explain WHY a domain's severity changed recently - Distinguish a sudden drop from a gradual decline Typically called after get_domain_full_data when the health status shows a problem and you need to understand its timeline.
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  • Get current Tour de France race phase. Returns one of: 'pre' (before the first stage, with nextStage + daysUntil), 'live' (a stage is currently running, with currentStage + elapsedPct), 'rest-day' (rest day between stages, with restDay + nextStage), 'between-stages' (off-day between consecutive stages, with lastStage + nextStage), 'finished' (race over, with finalStage).
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  • Worked-vs-On-time Execution Timeline (WOET) per-activity day-by-day classification of as-built execution against baseline. For each pairable activity (matched by ``task_code``), classifies execution into 4 day-states: - PROGRESS: work performed during the baseline-planned window - GAIN: work performed BEFORE the baseline window opened - EXTENDED: work performed AFTER the baseline window closed - VOID: baseline-window day where activity was NOT active This is a CPP-disclosed enhancement layered on top of AACE 29R-03 §3.3 Windows Analysis — a per-day execution classifier (Progress/Gain/Extended/Void) NOT itself AACE-defined. It is not a substitute for fragnet-based AACE 29R-03 §3.7 (TIA) modeling. It gives the trier-of-fact a calendar picture of how the project executed versus how it was supposed to execute, which is otherwise buried in finish-date deltas. Use this tool when you want a per-activity execution-quality picture (on-time %, count of activities with VOID days, etc.). Args: baseline_xer_path: server-side path to baseline XER (target dates). actual_xer_path: server-side path to as-built XER (act dates). baseline_xer_content: full text of baseline XER (alternative). actual_xer_content: full text of as-built XER (alternative). Supply EXACTLY ONE of path/content per pair. today: optional ISO date (YYYY-MM-DD) reference for in-progress activities. Defaults to actual XER's last_recalc_date if available, else today's date. Returns: { "method": "WOET", "standard": "AACE 29R-03 §3.3 Windows Analysis — per-day execution classification overlay (CPP-disclosed enhancement, not AACE-defined)", "today": "YYYY-MM-DD", "project_totals": {progress, gain, extended, void}, "per_activity": [{code, name, baseline_start, ...}, ...], "on_time_pct": float (0-100) }
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  • Post a job on the public job board for humans to discover and apply to. Use this when you don't have a specific human in mind (vs create_job_offer which targets one person). Humans browse the board, see your listing, and apply with a pitch. Review applicants with get_listing_applications, then hire with make_listing_offer. Requires agent_key. Rate limit: PRO = 5/day. Also suggested when search_humans returns no results.
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  • Remove all checked-off items from the shopping list at once. Use after a shopping trip when the user has bought everything marked. To remove a single item, use remove_shopping_list_item instead.
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  • Get the aggregate wash-report dataset: 30-day total active buyers, real-volume %, suspected_wash and self_test counts, full 8-label distribution, 14-day wash percentage time series, and five anonymized case studies (Service A through E) with pattern signals. For per-address real-time wash analysis with full signal breakdown, use the paid POST /api/v1/wash/check HTTP endpoint ($0.05 USDC) — that endpoint speaks x402, agents pay and receive data in a single HTTP round-trip.
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  • Event counts over time as date buckets. Returns [{ date, count }] sorted ascending. Granularity is automatic based on period length (hourly for ≤2 days, daily for ≤90 days, weekly for ≤365 days, monthly beyond) and can be overridden via `granularity`. Filterable to a specific event name (defaults to "pageview") and a single custom property key/value pair. Examples: - "pageview trend last week" → period="7d" - "signups per day this month" → event="signup", period="30d", granularity="day" - "hourly pageviews yesterday" → period="1d", granularity="hour" Limitations: forcing granularity="hour" over a 90-day period produces hundreds of buckets and may be truncated server-side. Buckets with no matching events return zero (the series does not skip missing dates).
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  • USE WHEN the user has just published a launch (or ≥24h post-launch) and asks 'what's next?', 'follow-up post?', 'iterate on this', 'plan day 2'. P13 — turns one launch into a 7-day launch mode: drafts a queue of follow-up actions (day 2 reply post, day 3 case study, day 5 metrics share, day 7 retrospective) grounded in the prior launch's runId, the per-tenant brand voice + repo facts + channel performance from the P9 company brain, and the prior recommendation (so the next move doesn't repeat the last one). Returns a draft queue + signed reviewUrl + the standard agentGuide block. Each next-move action lands in awaiting_approval; nothing fires without explicit human approval.
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  • Computes physical (23-day), emotional (28-day), and intellectual (33-day) biorhythm cycles for a birth date. SECTION: WHAT THIS TOOL COVERS Returns cycle values (-1.0 to +1.0), percentage, phase label (High/Rising/Falling/Low), and critical day flags for each cycle. Critical days occur when a cycle crosses the zero line — these represent instability and vulnerability to poor judgment or accidents. Supports single-day snapshot (days=1) and multi-day range (up to 90 days). Formula: sin(2π × t / cycle_length) where t = days since birth. Also returns composite_score (average of three cycles) and has_critical_day flag. Biorhythm is a Western concept — Vedic equivalents are Tarabala and Chandrabala (use asterwise_get_nakshatra_prediction for the Vedic equivalent). SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_nakshatra_prediction — for the Vedic personalized daily prediction. SECTION: INPUT CONTRACT birth_date (required): Date of birth in YYYY-MM-DD format. target_date (optional): Date to compute for. Defaults to today. days (optional int 1-90): Number of consecutive days. Default 1. SECTION: OUTPUT CONTRACT (single day) data.birth_date, data.target_date, data.days_since_birth (int) data.cycles{}: physical, emotional, intellectual — each: value (float -1.0 to +1.0), percentage (float), phase (string), is_critical (bool), cycle_length_days (int), description (string) data.critical_today[] (string array of cycle names that are critical) data.has_critical_day (bool) data.composite_score (float — average of three cycles) data.note (string — explains Vedic equivalents) SECTION: OUTPUT CONTRACT (date range, days > 1) data.birth_date, data.start_date, data.end_date, data.days data.daily[] — array of day objects each with date, cycles{}, critical[], composite_score SECTION: COMPUTE CLASS FAST_LOOKUP — pure math, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (upstream): birth_date after target_date → INTERNAL_ERROR INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_nakshatra_prediction — Vedic Tarabala/Chandrabala daily prediction. asterwise_get_panchanga — Vedic daily panchanga elements, not biorhythm cycles.
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  • Renew an ENS name or batch of names. Returns the transaction data needed to extend registration. Unlike registration, renewal is simple — just one transaction with payment. No commit/reveal needed. Accepts any duration from 1 day up — days, weeks, months, years. ENS protocol has no minimum renewal period; this tool floors at 1 day for safety. Examples: 1 day, 7 days (1 week), 28 days (1 month), 365 days (1 year). Anyone can renew any name (you don't need to be the owner). This is useful for: - Extending your own names before expiry - Gifting renewal to a friend's name - Protecting valuable names from expiring Returns exact on-chain pricing from the ETHRegistrarController with a 5% buffer (excess is refunded). For batch renewals (multiple names), all names are bundled into a SINGLE Multicall3 transaction.
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