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

• create_snapshot

  BorealHost

  Create a local container snapshot (async). Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed" or "failed". Available for VPS, dedicated, and cloud plans (any plan with max_snapshots > 0). Local snapshots are stored on the host disk and count against disk quota. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "snap-...", "status": "creating", "storage_type": "local", "message": "Snapshot started. Poll list_snapshots() to check status."} Errors: VALIDATION_ERROR: Max snapshots reached or insufficient disk quota

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• get_workout_detail

  Pace

  Shows all details of a single workout: heart rate, pace, cadence, power, intensity zones, elevation, calories, and more. Requires workout_id from get_workout_list. Also shows which sample data (HR time series, speed, GPS etc.) is available — these can be retrieved with get_workout_samples. Parameters: - workout_id: UUID of the workout from get_workout_list

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• x402_check_wash

  x402watch

  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. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.

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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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• 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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• ask_video

  Reka

  Ask a question about one or more videos with visual analysis. Most effective on focused time ranges — use start/end to specify the segment to analyze. BEFORE calling this tool, read the reka://docs/guide resource for recommended workflows. In most cases, you should first: - search_videos to find WHEN something happens, then pass those timestamps here as start/end - segment_video to detect and locate specific objects - get_transcript to read what was said For single-video questions, pass video_id with start/end. For cross-video questions, pass videos — a list of video references with start/end each. For follow-up questions, pass conversation_id from the previous response. You can add start/end to drill into a specific moment while keeping the conversation context. Requires qa_only or full pipeline.

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• get_graph_template_schemas

  RationalBloks

  Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 19 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_series_observations

  FRED Economic Data

  Get time series observations (data points) for a FRED series. Returns the actual data values for an economic indicator over time. Use search_series first to find the series_id, or use well-known IDs like UNRATE, GDP, CPIAUCSL, FEDFUNDS, MORTGAGE30US. For state unemployment, use state abbreviation + 'UR' (e.g. WAUR for Washington, CAUR for California). Results are sorted most-recent-first. For long series (e.g. daily data since 1954), use start_date/end_date to narrow the window or increase the limit up to 10000. Args: series_id: FRED series identifier (e.g. 'UNRATE', 'GDP', 'CPIAUCSL'). start_date: Optional start date in YYYY-MM-DD format (e.g. '2020-01-01'). end_date: Optional end date in YYYY-MM-DD format (e.g. '2024-12-31'). limit: Maximum observations to return (default 1000, max 10000).

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• list_segmentation_models

  Tenzro Ledger MCP

  List segmentation models currently loaded on this node (SAM 2, EdgeSAM, MobileSAM). Use list_segmentation_catalog to browse the curated catalog.

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• list_models

  Sats4AI - Bitcoin-Powered AI Tools

  Discover available AI models with numeric IDs, tier labels, capabilities, and per-call pricing in sats. Call this before create_payment to find the right modelId for your task. Returns JSON array: [{ id, name, tier, description, price, isDefault, category }]. Models marked isDefault=true are used when you omit modelId from create_payment. Filter by category to narrow results to a specific tool. This tool is free, requires no payment, and is idempotent — safe to call repeatedly.

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• chart_aq_trend

  Hermes — Air Quality Intelligence

  Generate a time series chart of air quality data. Returns a PNG chart image with a brief text summary. Use this when users ask about trends, patterns, or want to visualise air quality over time. Args: start_date: Start date (ISO format, e.g. "2025-01-01"). end_date: End date (ISO format). location: Postcode, place name, or "lat,lon". Provide this or site_code. site_code: Direct site code. Provide this or location. pollutants: Optional filter, e.g. ["NO2", "PM2.5"]. Defaults to NO2, PM2.5, PM10, O3 if not specified. frequency: "hourly", "daily", or "monthly" (default "daily"). show_who_guidelines: Show WHO guideline reference lines (default True). show_daqi_bands: Show DAQI band background shading (default True).

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• list-models

  Roundtable

  List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.

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• list_forecast_models

  Tenzro Ledger MCP

  List forecast (timeseries) models currently loaded on this node. Use list_forecast_catalog to browse available models from the curated catalog.

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• x402_check_wash

  x402watch

  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. Free tier. No payment required. Returns wash-filtered data using the same v2.0 algorithm as the paid endpoints.

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• edgar_company_facts

  livedatalink

  Get structured XBRL financial facts for a company. Without 'concept', returns the top-level facts catalog (concepts the company has reported). With 'concept' (e.g. 'Revenues', 'Assets', 'EarningsPerShareBasic'), returns the time series of values for that concept.

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• list-models

  Roundtable

  List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.

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• get_campaign_report

  discovery

  <tool_description> Get aggregated performance report for a media buy. Shows spend, impressions, clicks, conversions with time-series breakdown. </tool_description> <when_to_use> To check campaign performance metrics after activation. Supports period filtering and granularity control. </when_to_use> <combination_hints> list_media_buys → get_campaign_report for performance analysis. Pair with get_compliance_status for full campaign overview. </combination_hints> <output_format> Totals (spend, impressions, clicks, conversions) + time-series breakdown. </output_format>

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• search_series

  FRED Economic Data

  Search for FRED economic data series by keyword. Use this to find series IDs for economic indicators. For example, search 'unemployment rate' to find UNRATE, or 'gross domestic product' to find GDP. Returns series metadata including ID, title, frequency, units, and date range. Common series: UNRATE (unemployment), GDP (gross domestic product), CPIAUCSL (consumer price index), FEDFUNDS (federal funds rate), MORTGAGE30US (30-year mortgage rate), MEHOINUSA672N (median household income). Args: search_text: Keywords to search for (e.g. 'unemployment rate', 'GDP', 'inflation'). limit: Maximum number of results to return (default 10, max 1000).

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• deltasignal_atlas_history

  DeltaSignal ATLAS-7

  Use this read-only tool to retrieve a historical ATLAS-7 covenant and stress series for one crypto public company ticker. It returns one compact row per ATLAS source_date, including debt, crypto fair value, BTC holdings, stress, risk tier, live-price fields, quality flags, and provenance needed for mNAV and Mirror Pulse joins. Parameters: ticker is required; source_date_from and source_date_to bound the inclusive ATLAS source-date range; limit defaults to 500 and is capped at 2000; offset paginates the dated series. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not apply the latest ATLAS snapshot retroactively across history. Use this when the user needs historical ATLAS data, MSTR/Strategy time series, mNAV backtests, Mirror Pulse joins, or dated stress/risk snapshots.

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