"Guide to Creating Time Series Models with Prophet" matching MCP tools:

• get_data

  Abs Au

  Fetch observations from an ABS dataflow. dataKey is a dot-separated SDMX filter with one position per dimension (order from dataflow_structure); each position is a code, "+"-joined codes, or empty for wildcard. Pass "all" to fetch everything (can be large). Returns decoded series with their dimension labels and time-indexed values. Fetch dataflow_structure first to learn the dimension order and valid codes.

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• get_token_price_historical

  Teardrop

  Get historical price data for crypto tokens over a specified time window (1–365 days). Returns period statistics (start, end, % change, high, low) plus a downsampled daily price series. Use for period comparisons (month-over-month, YTD), trend analysis, and price charts. Prefer over web_search for any time-comparative financial query. Pass stats_only=true when the daily series is unnecessary. price_change_pct is pre-computed and should not be re-derived with calculate.

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• show_fx

  opula

  Inspect the FX rate cache (foreign per 1 USD). Without `currency`, returns per-currency coverage (count + first/last date). With `currency` + `date`, returns the point-in-time rate, falling back to the most recent within `lookback_days`. With `currency` + `from`/`to`, returns the cached series for that range. With `currency` alone, returns the `limit` most-recent rows. USD has no rows (it's the base, always 1.0).

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• stablecoin_flows

  whale-insights

  Get current stablecoin supply & 24h net flows — USDT, USDC, DAI from DefiLlama (use stablecoin_history for daily time-series) — Current stablecoin circulating supply plus 24h net change (positive = expansion/inflow, negative = contraction/outflow) for USDT, USDC, DAI, and other major stablecoins, sourced from DefiLlama stablecoins.llama.fi. Use this endpoint for the current snapshot; use /api/public/stablecoin-history for daily time-series data (up to 180 days); use /api/public/stablecoin-monthly for long-term monthly trends. Cached ~30min.

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• get_page_trend

  RevenueScope: Japanese EC RPS Benchmarks

  Return how ONE page's Google Search performance changed over time (FD-040) — the time-axis drill-down for a page surfaced by get_breakdown(dimension='page'). Given a `page` (a normalized path like '/news/rps-revenue-per-session-guide' or a full URL — both resolve), returns a `series` of day or week buckets, each with clicks, impressions, and impression-weighted avg_position, plus a `summary` (first/last/best/worst position, position_delta, click & impression totals). avg_position is a RANK: smaller is better, so a NEGATIVE position_delta means the page's ranking IMPROVED over the window (e.g. 12.0 → 9.0 = delta −3.0). Use this to verify whether SEO work on a page paid off (rank rose / clicks grew) or slipped. Buckets where the page never appeared in search are omitted (gaps), so the series can be shorter than the period. `granularity` defaults to 'day' for windows up to ~35 days and 'week' for longer (weekly smooths daily noise); pass it to override. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Google-search only; data lags 1-2 days. This is per-page; for the cross-page snapshot use get_breakdown(dimension='page'), and for per-query (keyword) trends use get_keyword_performance.

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• get_facility_risk_delta

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks what has CHANGED in a facility's (or its market's) risk profile recently — "has this site gotten riskier lately?", "which way is this market moving?" — a temporal question static-trained models can't answer. Returns the REAL DCPI market-health delta (excess-power score change over the window, direction improving/worsening/flat) from DC Hub's history-preserving daily snapshots. INTEGRITY: only DCPI market-health has a short-term temporal series; the site-hazard dimensions (FEMA disaster / USGS seismic / NOAA climate / WRI water) are DECLARED static (they don't change week-to-week) with a pointer to the point-in-time tool — never a fabricated week-over-week delta; no snapshot history → coverage:unavailable. Params: facility_id (a discovered-facility id or slug) OR market (a market name/slug), since (e.g. "7d"/"30d", default 7d). Returns {facility, dcpi_market_health:{delta, now, direction, coverage}, static_dimensions{...}, summary}. For the current point-in-time risk (not the change) use get_composite_site_score / get_disaster_risk / get_climate_intel.

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• get_datetime

  local-mcp

  Get the current date and time of the machine where LMCP runs — with timezone and UTC offset. Call this whenever you need the real 'now' on the user's computer: before creating calendar events or reminders, resolving relative dates like 'today'/'tomorrow'/'next Friday', or timestamping. Takes no arguments.

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• openmeteo_get_climate

  open-meteo-mcp-server

  Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: "CMCC_CM2_VHR4", "FGOALS_f3_H", "HiRAM_SIT_HR", "MRI_AGCM3_2_S", "EC_Earth3P_HR", "MPI_ESM1_2_XR", "NICAM16_8S". With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled.

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• get_facility_risk_delta

  DC Hub — Data Center & Power Intelligence

  Use when a user asks what has CHANGED in a facility's (or its market's) risk profile recently — "has this site gotten riskier lately?", "which way is this market moving?" — a temporal question static-trained models can't answer. Returns the REAL DCPI market-health delta (excess-power score change over the window, direction improving/worsening/flat) from DC Hub's history-preserving daily snapshots. INTEGRITY: only DCPI market-health has a short-term temporal series; the site-hazard dimensions (FEMA disaster / USGS seismic / NOAA climate / WRI water) are DECLARED static (they don't change week-to-week) with a pointer to the point-in-time tool — never a fabricated week-over-week delta; no snapshot history → coverage:unavailable. Params: facility_id (a discovered-facility id or slug) OR market (a market name/slug), since (e.g. "7d"/"30d", default 7d). Returns {facility, dcpi_market_health:{delta, now, direction, coverage}, static_dimensions{...}, summary}. For the current point-in-time risk (not the change) use get_composite_site_score / get_disaster_risk / get_climate_intel.

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• GetFredSeriesData

  Fintel Discovery — Financial Intelligence for AI Agents

  Fetch time-series observation data from FRED for a specific economic series. Returns date + value pairs with series metadata (title, units, frequency). Use SearchFredSeries first if you don't know the series ID. Use this tool when: - You need historical macro data (rates, inflation, GDP, unemployment) - You want to provide macro context alongside advisor or fund data - You are comparing economic conditions across time periods - You need the current value of a key economic indicator Pass observation_start / observation_end to limit the date range. Pass frequency to aggregate (e.g. 'm' for monthly, 'q' for quarterly). Requires FRED_API_KEY environment variable (free at fred.stlouisfed.org). Source: Federal Reserve Bank of St. Louis FRED API.

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• info_platformmetrics_get_platform_info

  Gate Info MCP

  One-shot protocol profile by name and scope. scope=full adds competition_metrics{} for CEX venues (spot/derivs/depth/OI core+extended/PoR). Set include_oi_symbol_detail=true with oi_symbol_limit (1-100, default 20) for top-N OI breakdown. Ranked multi-protocol list→search_platforms. Daily time series→get_platform_history.

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• xmp4_guide

  xmp4 — Semantic code knowledge for your stack

  Usage guide for xmp4 tools — read this first to learn the correct workflow

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• bls_get_latest

  bls-labor-mcp-server

  Return the single most recent observation for one or more BLS series. Use for "what is X right now" questions — the current unemployment rate, the latest CPI reading, etc. Each series consumes one API query against the 500/day limit; for the current value of many series, bls_get_series with a 1-year window is more quota-efficient (one query for up to 50 series). Recommended limit: 10 series; maximum: 50.

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• search_ai_models

  dynamic-feed

  Search current AI models by price, context window, and capability. Use this for up-to-date model pricing/features you don't reliably know. Prices are USD per 1M tokens. Results are cheapest-input-price first. Args: query: match part of a model name/id (e.g. "haiku", "gpt"). provider: filter to one provider (openai, anthropic, google, xai, mistral, deepseek, groq). max_input_price: only models at or below this USD/1M input price. min_context: only models with at least this context window (tokens). needs_vision: only models that accept images. limit: max results. Envelope: this searches our model-pricing registry, so measured_at = when the freshest matching row was last refreshed (each row's `updated_at`); max_age 18h covers the 12h registry-refresh cycle so a current row never falsely reads "stale". A search returning nothing yields unavailable — there's no honest observation time to claim. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• list_series

  Un Sdg

  Flat list of all SDG data series (series code + description + goal/target/indicator it belongs to). Series codes are what you pass to get_series_data. Use this when you want the raw series list rather than the indicator-nested view from list_indicators.

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• water_get_series

  usgs-water-mcp-server

  Get a time series of daily or instantaneous values for a USGS site and parameter over a date range. Returns siteNumber, parameterCd, and time-ordered value records. When the server has DataCanvas enabled, large result sets (>500 records) spill to a canvas — the response includes canvas_id and table_name for SQL analysis via water_dataframe_query. Without DataCanvas, returns the most recent 500 records with truncated=true. Use water_find_sites to discover valid site numbers. Use water_list_parameters for parameter codes.

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• list_saved_searches

  FundingLandscape

  List all saved searches for the current user. Returns each search with its ID, query, filters, alert settings, and last run time. Use this FIRST to check what the user already has before creating or updating searches. Response includes remaining slots and plan info. Saved searches are available on every plan, including Free (Free: 1 saved search with weekly email alerts, Plus: 10, Pro: 25). Does not count toward your monthly searches.

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• create_sleepwalker_visibility_run

  Sleepwalker

  Queue a saved AI Visibility run from prompts x platforms or explicit probes. Use this when the user wants reportable probe results, not just prompt ideas. This starts queued work and returns quickly with a run_id; poll get_sleepwalker_visibility_run_status until the run is terminal instead of creating duplicate runs. Platforms accept canonical slugs or common labels: perplexity, openai/ChatGPT, grok, gemini. To pin AI models, pass the models map with matrix mode (per platform) or set model on explicit probes (per probe) — values are model ids from list_sleepwalker_visibility_models or the keywords latest / prior / default. Default-model probes cost 1 credit; each non-default model has its own per-probe price (varies by model — call list_sleepwalker_visibility_models for the exact credits_per_probe before quoting cost). Results record the exact model that answered each probe. The response includes credit fields when credits are reserved.

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