Returns historical daily closing prices for any supported cryptocurrency over 30, 90, or 365 days. Use for trend analysis, drawdown calculation, or training data. Source: CoinGecko. Priced at $0.15 USDC via x402.
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607,235 tools. Updated 2026-09-24 14:09
"Historical Cryptocurrency Data and Sector Trend Analysis" matching MCP tools:
- Get historical price data for a cryptocurrency with trend analysis including period high/low, price change percentage, and volatility metrics over a customizable timeframe.ConnectorOAuth
- AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector. - sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested) - history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only) - sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) — the same underlying number `signal` buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call. - sector_score_trend: {change_7d, change_30d, direction} — whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only — this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday morning or after a holiday. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.ConnectorNo auth
- AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector. - sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested) - history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only) - sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) — the same underlying number `signal` buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call. - sector_score_trend: {change_7d, change_30d, direction} — whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only — this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday morning or after a holiday. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.ConnectorNo auth
- List analysts at a given firm (case-insensitive substring match). Each row: {name, firm, rank, avg_return_1y_pct, covered_tickers_sample, total_covered}. Results are deduped by analyst name (keeping the best rank) and sorted by rank ascending. When `sector` is provided, the per-analyst coverage list is filtered to tickers in that sector and the row shape becomes {name, firm, rank, avg_return_1y_pct, sector, covered_tickers}. Args: firm: Firm name or fragment (e.g. 'Goldman' matches 'Goldman Sachs'). sector: Optional lowercase sector (e.g. 'technology', 'healthcare').ConnectorNo auth
- Sector rotation context for Hood tokenized stocks (API-only tracker refreshed every 3h): market regime (risk_on|risk_off|mixed), an 11-SPDR sector score/label/flow board and a per-ticker join — sector score (0-100), label (inflow|interest|neutral|outflow_leaning|outflow), flow trend, theme matches + live basis. Answers where money is rotating and whether your token's sector is in inflow or outflow. tickers: CSV filter (empty = full Hood universe).ConnectorNo auth
Matching MCP Servers
- AlicenseNot gradedqualityBmaintenanceLive US sector rotation for agents: 30 US equity sector baskets ranked by average move every session, versioned basket rosters, and a daily close record since July 2026 that is never backfilled. Hosted MCP server (Streamable HTTP); this repository holds the docs, config and registry server.json.MIT
- AlicenseNot gradedqualityCmaintenanceMCP server for trend-pulse, an agentic trend intelligence platform that fetches and analyzes trending topics from 37 sources, provides search, historical data, and lifecycle prediction via 29 tools.186 PyPI60MIT
Matching MCP Connectors
Live US sector rotation: 30 sector baskets ranked every session, versioned rosters, daily record.
Calculate a metric trend summary
- Purpose: Level 2 (ETF / basket / sector granularity — Level 1 is individual symbols) prediction calibration. Returns hit_rate_ema per (market, group, interval, regime_bucket) with sample counts, **plus the majority-class baseline needed to interpret them**. This is measurement, NOT a claim of edge — as of 2026-09-18 the measured skill (accuracy minus baseline) is negative in all three markets. Triggers (casual questions too): "how good are your sector calls?", "섹터 예측 잘 맞아?", "sector rotation accuracy?", "그룹 단위 적중률 보여줘", "can you time sector moves?". When to call: when an AI wants to see Layer D evidence (Layer D = sector-structure tier of the 5-layer trust pyramid). Prerequisites: none. Next steps: get_structure_validation_history for the daily trend. Caveats: empty until structure-learning cycles complete. Rows are paginated — read `total_available` (not `len(calibration)`) for the whole-set size. `baseline`, `meta.total_entries` and `meta.total_samples` are always whole-set. Rows keep their dimension keys (market_id / interval / regime_bucket); they are never hoisted out of the row.ConnectorNo auth
- Fetch a specific Agentberg skill pack by name. Critical skills (regime, risk_calendar, health) are automatically bundled in get_skills. Optional skills: 'rotation' for sector money-flow analysis, 'narrative' for macro headline synthesis.ConnectorNo auth
- Search across your own ideas and the public marketplace by keyword, type, VC score range, sector, claim status or visibility. Returns rich results with a description snippet, trend and claim status so you can spot ideas worth claiming. Read-only and free; leave the query empty to browse with filters only.ConnectorNo auth
- Calculate prior-year and exact five-year changes for one final annual USDA NASS national crop series. Returns component observations, absolute and percent changes, five-year CAGR when valid, exact units, evidence hash, and caveats. Use this for 'how has corn yield changed?', 'five-year soybean production trend', or 'trend in upland cotton planted acres'. The optional as-of year never mixes future releases into a historical result.ConnectorNo auth
- Query World Bank indicator values for one or more countries across a time range — the primary data-access tool; find indicator_id values with worldbank_search_indicators. Observations carry a null value where data is not available for a country×year cell, which is common for sparse series. Set either date_range (historical analysis) or mrv (most recent N values), not both. For "all" countries, page through the results (per_page up to 1000), since the API returns several hundred entries per indicator. Indicators the standard data endpoint does not serve — WDI Database Archives, PEFA, ICP, GDLD, International Debt Statistics: DSSI, Food Prices for Nutrition — are answered from their own dataset instead; the response then carries sourceScoped, naming that dataset and the release, classification, sector, or counterpart area applied (see dimension_value), because those values can be archived or superseded figures rather than current ones.ConnectorNo auth
- Get daily narrative/sector history — top crypto market sectors ranked by market cap change %, strength, and token count over up to 90 days — Daily historical narrative strength per market sector (e.g. DeFi, Layer 2, AI, Meme Coins) from CoinGecko Categories. One row per day per sector: market cap change %, strength score (0-100), token count in sector, daily rank, and top tokens. Filter by ?sector= for a single sector trend. Useful for identifying which narratives are accelerating or fading. DB-backed, 5-min cache. Powered by narrative_daily table (365d retention, permanent monthly archive). — Use this for daily historical data; use the corresponding live snapshot tool for current conditions and the monthly tool for long-term trends.ConnectorNo auth
- Forecast a business metric horizon periods ahead from its historical series and return per-period point forecasts with confidence intervals. The trend comes from the last up-to-6 history values, volatility from the mean absolute period change, and a 5000-scenario simulation quantifies uncertainty; seasonality=true applies an alternating +/-5% seasonal factor. Use query_data to build the history from a connected dataset first. Synchronous deterministic compute; nothing is persisted. Returns baseline (most recent value), forecast_mean (final period), total_change_pct, and one {period, forecast, lower_bound, upper_bound, trend} item per period with trend up, down, or stable.ConnectorNo auth
- Get HISTORICAL price data / trends for one model. This is InferenceIndexer's differentiator: aggregators like OpenRouter expose only current price; this returns the price over time (input, output, blended $/M), enabling trend analysis. Args: model_id: Canonical model id, e.g. 'openai/gpt-5.6'. days: History window in days (1-365, default 30; plan-dependent). Returns: historical price series for the model.ConnectorNo auth
- Retrieves real-time price data for any cryptocurrency listed on CoinGecko. Returns the current price in any fiat currency, 24-hour percentage change, market capitalisation, and 24-hour trading volume. Supports all major cryptocurrencies including Bitcoin (BTC), Ethereum (ETH), Solana (SOL), XRP, Cardano (ADA), Dogecoin (DOGE), Polygon (MATIC), Chainlink (LINK), Avalanche (AVAX), and 10,000+ additional coins. Use crypto_price when an agent needs the full market picture for a digital asset — price, change, market cap, and volume in one call. Prefer crypto_price_lite when only the spot price and 24h change are needed and a smaller response payload is preferred. Use crypto_fx_rates (via CoinAPI) when converting a specific amount between a cryptocurrency and fiat, or between two cryptocurrencies. Do not use this tool for fiat-to-fiat currency conversion (e.g. USD to EUR) — use currency_convert instead. Do not use when historical price data for a specific past date is required — this tool returns live spot prices only.ConnectorNo auth
- Filter and screen stocks based on financial criteria like market cap range, sector, P/E ratio thresholds, dividend yield, or revenue growth. Returns matching ticker symbols with key metrics. Use for value investing, growth stock identification, or portfolio rebalancing analysis.ConnectorNo auth
- Retrieves real-time price data for any cryptocurrency listed on CoinGecko. Returns the current price in any fiat currency, 24-hour percentage change, market capitalisation, and 24-hour trading volume. Supports all major cryptocurrencies including Bitcoin (BTC), Ethereum (ETH), Solana (SOL), XRP, Cardano (ADA), Dogecoin (DOGE), Polygon (MATIC), Chainlink (LINK), Avalanche (AVAX), and 10,000+ additional coins. Use crypto_price when an agent needs the full market picture for a digital asset — price, change, market cap, and volume in one call. Prefer crypto_price_lite when only the spot price and 24h change are needed and a smaller response payload is preferred. Use crypto_fx_rates (via CoinAPI) when converting a specific amount between a cryptocurrency and fiat, or between two cryptocurrencies. Do not use this tool for fiat-to-fiat currency conversion (e.g. USD to EUR) — use currency_convert instead. Do not use when historical price data for a specific past date is required — this tool returns live spot prices only.ConnectorNo auth
- Use when current energy price data is needed for a commodity brief, input cost analysis, or energy sector context in a CFO or investment brief. Returns WTI crude and natural gas spot prices when EIA API is configured. Example: WTI crude $78.40/bbl, natural gas $2.31/MMBtu — energy input costs 12% below year-ago levels, favorable for manufacturing and transportation operating margins. Source: US Energy Information Administration. $0.02 USDC per call.ConnectorNo auth
- Generate predictive insights from observation patterns. Predict whether a venue is likely to see increased foot traffic based on current patterns. Uses historical observation_stream data to compute trend analysis via linear regression on time-bucketed metrics. Generates predictions with confidence intervals based on the observed trend, variance, and sample size. WHEN TO USE: - Predicting future audience patterns at a venue or screen - Forecasting foot traffic trends for campaign planning - Understanding whether metrics are trending up, down, or stable - Making data-driven decisions about inventory and pricing RETURNS: - prediction: The predicted trend and expected values - trend: 'increasing' | 'decreasing' | 'stable' - current_avg: Current average metric value - predicted_avg: Predicted average over the time horizon - change_pct: Expected percentage change - confidence_interval: { lower, upper } bounds - confidence: Overall prediction confidence (0-1) - supporting_data: Recent data points that inform the prediction - data_points: Array of { bucket, avg_value, sample_count } - total_observations: Total observations analyzed - methodology: Description of the prediction approach - suggested_next_queries: Follow-up queries to refine the prediction EXAMPLE: User: "Will this QSR venue see more foot traffic next week?" predictive_query({ question: "Will foot traffic increase at QSR venues?", venue_type: "restaurant_qsr", time_horizon: "7d" }) User: "Predict audience attention trends for this screen" predictive_query({ question: "What will audience attention look like?", screen_id: "507f1f77bcf86cd799439011", time_horizon: "3d" })ConnectorNo auth
- Filter and screen stocks based on financial criteria like market cap range, sector, P/E ratio thresholds, dividend yield, or revenue growth. Returns matching ticker symbols with key metrics. Use for value investing, growth stock identification, or portfolio rebalancing analysis.ConnectorNo auth
- [Sales Intelligence] Track recent startup funding announcements filtered by stage and sector. Wraps `nexgendata/startup-funding-tracker`. Returns recent rounds (Crunchbase News + TechCrunch + sector press) including company, amount, round type, investors, and date. Args: stage: Optional stage filter ("seed", "series a", "series b", ...). sector: Optional sector / industry filter ("ai", "fintech", ...). days_back: Look-back window in days (default 30, max ~180).ConnectorNo auth