"Accessing personal health data from Google and checking historical cryptocurrency prices" matching MCP tools:

• get_historical_price

  Pyth Pro MCP Server

  Get price data for specific feeds at a historical timestamp. Use get_symbols first to find feed IDs or symbols. If both price_feed_ids and symbols are provided, only price_feed_ids are used. Accepts Unix seconds, milliseconds, or microseconds (auto-detected). Historical data is available from April 2025 onward — do not request timestamps before that. The timestamp is internally converted to microseconds and aligned (rounded down) to the channel rate — e.g. for fixed_rate@200ms, it must be divisible by 200,000μs. Prices are integers with an exponent field — human-readable price = price * 10^exponent. Pre-computed display_price fields are included for convenience.

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• validate_e164_number

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Parse a phone number, normalize to E.164, and classify it. International coverage is via libphonenumber-js (every country, line type when known). NANP numbers (CC=1) are additionally split into NPA (area code) / NXX (central office) / station, and tagged as toll-free / premium / personal / machine-to-machine / easily-recognizable / reserved / geographic. Use when validating `From` / P-Asserted-Identity / SHAKEN `orig.tn`, deciding whether an outbound call needs full attestation, or sanity-checking caller ID format. Pair with: `lint_sip_request` to validate that PASSporT `orig.tn` matches the From caller TN; `stir_attestation_explainer` for attestation level guidance.

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• vf_health

  VoiceFlip

  Returns VoiceFlip MCP server health and version metadata. No authentication required. Use this first to verify the server is reachable from your MCP client.

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• get_domain_activity_health

  DMARKOFF — DMARC, SPF & DKIM Analytics

  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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• list_data_sources

  Indicate

  Step 2 — List data sources available within a tenant. (In the Indicate system a data source is called a 'data product'.) Examples: Google Analytics, Facebook Ads, vioma, Booking.com. Returns each data source's 'id', 'displayName', and 'semantic_context_id'. → Pass the chosen 'id' as 'data_source_id' and 'semantic_context_id' to list_metrics.

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• parent_calendar_subscribe_url

  Lodi Kids Activities

  Returns the parent's personal iCal feed URL — subscribe in Google/Apple/Outlook Calendar to see practices + games in your normal calendar app. Refreshes hourly.

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• secedgar_get_financials

  secedgar-mcp-server

  Get historical XBRL financial data for a company. Accepts friendly concept names (e.g., "revenue", "net_income", "assets") or raw XBRL tags. Discover available friendly names with secedgar_search_concepts. Handles historical tag changes and deduplicates data automatically.

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• get_analytics

  DPX — Institutional Cross-Border Settlement

  Get live DPX performance analytics. Returns current stability score, ESG composite scores, live fee breakdown, oracle health across all data sources, and a settlement readiness assessment. Use for dashboards, reporting, and AI-driven monitoring of protocol health.

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• ava_get_gas_prices

  Ava Genesis Agent API

  Get real-time gas prices and total deployment cost estimates across all supported chains. Use this before deploying to pick the cheapest chain and avoid gas spikes. No API key required. Returns: gasPrice (gwei), estimatedDeployGas, totalCostEth, totalCostUsd, and platformFeeUsd for each chain. Data is fetched live from each chain's RPC — prices reflect current mempool conditions. Cache TTL: 15 seconds. If a chain RPC is unreachable, that chain is omitted from the response. Base and BNB Chain consistently have the lowest fees; Ethereum mainnet the highest.

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• get_indicator

  American Default Research

  Fetch a compact snapshot of an American Default economic indicator by slug. Returns latest value, unit, frequency, direction, pre-computed aggregates (period averages, extremes, sustained runs), editorial prose (when available), and canonical APA / MLA / Chicago / news-copy citations. Raw historical series is NOT included — use https://americandefault.org/api/indicators/{slug}.json for the full data. Slug examples: 'the-buffer' (personal savings rate), 'mortgage-delinquency', 'initial-unemployment-claims-sa'.

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• network_status

  Bitget MCP — Crypto, DeFi & Macro Market Intelligence

  On-chain public data (no API key): ETH gas prices, BTC recommended fees, mempool stats, and recent blocks.

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• currency_rates

  Inferventis MCP Server

  Retrieves live and historical fiat currency exchange rates from the Frankfurter API (sourced from the European Central Bank). Supports both real-time conversion and historical rate lookup for any past date, making it the preferred tool when auditable, ECB-sourced rate data is required. Use currency_rates when a rate from a specific past date is required (e.g. accounting, tax, or audit), or when the ECB source must be documented. Prefer currency_convert when only a live conversion is needed with a richer structured response. Prefer currency_convert_lite for lightweight live ECB conversions without historical requirements. Prefer currency_fx_lite when ECB sourcing is not required and only a lightweight live result is needed. Use currency_convert_open when a non-ECB rate source is acceptable and Frankfurter is unavailable. Does not support cryptocurrency pairs — use crypto_fx_rates for any conversion involving a digital asset.

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• asterwise_get_personal_cycles

  Asterwise

  Returns the Personal Year, Personal Month, and Personal Day numbers for a given birth date and optional target date. All three cycle numbers are derived from the birth month, birth day, and the target calendar date. SECTION: WHAT THIS TOOL COVERS Personal cycles are the Pythagorean timing system. The Personal Year (1–9) sets the annual theme. The Personal Month refines it to a 30-day window. The Personal Day gives the daily energy flavour. A Personal Year 1 favours new beginnings; a 9 favours completion and release. Cycles nest: the same number in Year, Month, and Day simultaneously creates a peak intensity day. Formula: Personal Year = birth_month_reduced + birth_day_reduced + target_year_reduced Personal Month = Personal Year + target_month, reduced Personal Day = Personal Month + target_day, reduced Master numbers 11 and 22 are preserved where they arise. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_numerology_profile — see personal cycles alongside core numbers. SECTION: INPUT CONTRACT date — Birth date in YYYY-MM-DD format. Example: '1985-11-12' year (optional int) — Target year. Defaults to current calendar year. Example: 2026 month (optional int 1–12) — Target month. Defaults to current month. Example: 5 day (optional int 1–31) — Target day. Personal Day is only returned when day is provided. Defaults to null (Personal Day omitted). Example: 1 SECTION: OUTPUT CONTRACT data.personal_year (int — 1–9 or master 11/22) data.personal_month (int — 1–9 or master 11/22) data.personal_day (int or null — null when day parameter is not provided) data.target_year (int — echoed) data.target_month (int — echoed) data.target_day (int or null — echoed) SECTION: RESPONSE FORMAT response_format=json — structured JSON. response_format=markdown — human-readable. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — all validation is upstream. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_personal_year — returns Personal Year only, no month or day breakdown. asterwise_get_numerology_profile — core name numbers; personal_year field is null there.

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• get_price_statistics

  Cenogram - Polish Real Estate Data

  Get price per m² statistics by location for residential apartments in Poland. Note: only covers residential units (lokale mieszkalne). For other property types, use search_transactions. 'Warszawa'/'Kraków'/'Łódź' auto-expand to all sub-districts (Warszawa=19, Kraków=5, Łódź=6). Other names use partial match. Data quality: based on transaction prices from notarial deeds, not asking/listing prices. Coverage varies by county (some have data gaps of 5+ years).

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• get_crypto_prices

  CerebroChain Supply Chain & Market Data

  Get real-time cryptocurrency prices (BTC, ETH, SOL) from CoinGecko. Free — no API key needed.

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• get_dataset_stats

  Trillboards DOOH Advertising

  Get statistics about available causal training data: total tuples, unique creatives, venue diversity, date range. Queries observation_stream for rows that have both a creative ID and a VAS outcome recorded, giving a picture of how much training data is available for the causal prediction engine. WHEN TO USE: - Checking if enough data exists for reliable causal predictions - Understanding the diversity of training data (creatives, venues, time range) - Monitoring causal dataset health and growth - Planning data collection strategies RETURNS: - data: Dataset statistics - total_tuples: number of context-action-outcome records - unique_creatives: number of distinct creatives with VAS data - unique_venue_types: number of distinct venue types represented - date_range: { start, end } of available data - observations_per_creative: { min, max, mean, median } distribution - metadata: { query_window_days } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How much causal training data do we have?" get_dataset_stats({})

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• query_dataset

  opendata-cat

  Query real data from a dataset. Check instructions for featured dataset_ids and NOTES section for common filter patterns (municipal budgets, contracts, weather, energy, fuel prices).

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• get_energy_forecast

  SupplyMaven MCP Server

  Get the US Energy Information Administration's Short-Term Energy Outlook (STEO) — official government forecasts for energy production, consumption, and pricing. Returns both historical actuals and forward-looking projections for crude oil prices, natural gas prices, electricity generation, renewable energy production, and petroleum consumption. The STEO is the most widely referenced energy forecast in the world. Distinguishes actual historical data from projected forecasts using the isActual flag. Used by energy traders, logistics companies budgeting fuel costs, and macro analysts.

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• get_tract_measures

  CDC PLACES Health Data

  Get all CDC PLACES health measures for a census tract. Returns tract-level estimates for health outcomes, behaviors, preventive services, and health status indicators. Tract-level data uses small area estimation and may have wider confidence intervals than county data. Args: tract_fips: 11-digit census tract FIPS code (e.g. '53033005300'). Must be a string, not an integer. year: Optional release year to filter by. Omit for the most recent data.

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• waveguard_scan_timeseries

  WaveGuard

  Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies. Typical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift

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