"An MCP server for crypto trading with historical data and candlestick patterns" matching MCP tools:

• get_ohlcvB

  openmm-mcpA

  Get OHLCV (candlestick) data for a trading pair

  ConnectorNo auth

• patternfetch_scanA

  patternfetchA

  Scan US stocks, ETFs, and crypto for tickers currently in a given regime or showing a chart/candlestick pattern, RANKED by the honest backtested base rate + 95% CI — discovery, NOT lookup. This is the screener: instead of asking about one ticker you already know, ask "which tickers right now are in an uptrend / printing a double_bottom, and which of those has the strongest historical base rate?" and get a ranked shortlist back. Precomputed daily over a curated universe (liquid US large-caps + core/sector ETFs + major crypto pairs) so it is fast and cheap. Filters (all optional): assetClass ("stock"|"crypto"|"all"), regime ("up"|"down"|"range"), pattern (e.g. "double_bottom","double_top","head_and_shoulders","bullish_engulfing","bearish_engulfing","hammer"), minLift (-1..1 in rate points, e.g. 0.02 = keep only patterns beating their OWN pattern-free baseline by >= 2pp; 0 = at or above baseline), minBaseRate (0..1, drop tickers whose top pattern base rate is below this), tf, limit. PREFER minLift over minBaseRate: a raw base rate is not comparable across bullish and bearish rows, so minBaseRate:0.55 mostly returns bullish patterns in a rising universe before any of them carries information, whereas minLift returns the ones that measurably add something. Rows with no baseline in the evidence table are excluded by any minLift (absence of a lift is not a lift of 0). Each row: {sym, tf, assetClass, regime, pattern, baseRate, ci95, n, scope, confidence, asOf} PLUS the drift-free comparison {baseline, lift, liftCi95, liftReading} — baseline is the direction-matched rate with no pattern present, lift is baseRate minus that baseline, and liftReading says whether the difference is distinguishable from zero at all ("above-baseline" | "below-baseline" | "indistinguishable-from-baseline"). Read lift, not baseRate, when comparing a bullish row against a bearish one: in a rising universe a bullish pattern starts ahead before it carries any information. Ranked by baseRate desc, then confidence desc, then narrower CI, then fresher asOf. WHEN: an agent wants to FIND candidates across the market, not analyze a named one (then call brief on the shortlist). WHEN NOT: you already have a specific ticker (use brief). Example: {"assetClass":"all","regime":"up","minLift":0.02,"limit":20}. Impersonal historical data, not investment advice; base rates are gross directional frequencies and do not guarantee future results.

  ConnectorNo auth

• team.summarizeA

  AI Design BlueprintA

  Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate readiness trend. WHEN TO CALL: the user asks 'how is the Blueprint helping me/my team', 'what should I explore next', or 'show me my Blueprint usage'. WHEN NOT TO CALL: proactively or on every conversation turn (the summary is an explicit retrospective, not telemetry); to compare users (returns only the caller's own data). BEHAVIOR: read-only, idempotent over the same window. Aggregates from AIToolCallLog + ValueEvent + AIValidationRunLog. Pass private_session=true to bypass server-side logging for this summary call (the underlying historical data still exists; only this read is untracked). Auth: Bearer <token>, Pro or Teams plan. UK/EU residency.

  ConnectorNo auth

• recommend_toolA

  ~Alter IdentityA

  Get the wiring instructions for connecting an MCP client to ~alter. The name is historical and this recommends nothing: it hands back connection details, not a suggested tool. Use it when adding ~alter to a new MCP client, or when passing the endpoint to another agent so it can connect for itself. Returns the MCP endpoint URL, a ready-to-paste JSON configuration snippet, and how many tools are callable at each tier. Takes no parameters and reads no member data. Free L0, no authentication required.

  ConnectorNo auth

• team.summarizeA

  AI Design Blueprint DoctrineA

  Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate readiness trend. WHEN TO CALL: the user asks 'how is the Blueprint helping me/my team', 'what should I explore next', or 'show me my Blueprint usage'. WHEN NOT TO CALL: proactively or on every conversation turn (the summary is an explicit retrospective, not telemetry); to compare users (returns only the caller's own data). BEHAVIOR: read-only, idempotent over the same window. Aggregates from AIToolCallLog + ValueEvent + AIValidationRunLog. Pass private_session=true to bypass server-side logging for this summary call (the underlying historical data still exists; only this read is untracked). Auth: Bearer <token>, Pro or Teams plan. UK/EU residency.

  ConnectorNo auth

• patternfetch_briefA

  patternfetchA

  Get a token-compact market-state brief for a stock, ETF, or crypto ticker + timeframe. Returns compact candles, detected chart/candlestick patterns with geometric confidence AND a backtested historical base rate (how often that pattern+timeframe+confidence-band actually resolved its way), support/resistance levels, trend/regime, and interpreted indicators (RSI/EMA state) plus a one-line summary. Covers US stocks/ETFs (split & dividend adjusted, delayed/EOD) and crypto spot (realtime). WHEN: an agent needs the current technical picture of a market without dumping raw OHLCV into context (saves tokens, avoids numeric hallucination). WHEN NOT: you need order execution or portfolio advice. Examples: {"ticker":"AAPL","timeframe":"1d"}, {"ticker":"BTC/USDT","timeframe":"4h"}. Output is impersonal market data, NOT investment advice.

  ConnectorNo auth

• get_coingecko_coins_id_ohlcA

  AIsa FinanceA

  Candlestick data for one coin as a bare array of rows, each row positional: timestamp in milliseconds, then open, high, low and close. There are no field names in the response and no volume. Candle width is derived from `days`, which is required. Use it for candle or technical analysis. For a price line together with market cap and traded volume use `get_coingecko_coins_id_market_chart` instead.

  ConnectorOAuth

• get_coingecko_coins_id_ohlcA

  AIsa Crypto Market DataA

  Candlestick data for one coin as a bare array of rows, each row positional: timestamp in milliseconds, then open, high, low and close. There are no field names in the response and no volume. Candle width is derived from `days`, which is required. Use it for candle or technical analysis. For a price line together with market cap and traded volume use `get_coingecko_coins_id_market_chart` instead.

  ConnectorOAuth

• get_historical_reaction_contextA

  FX ContextA

  Summarize the aggregate post-event reaction pattern across historical macroeconomic events similar to a selected target release. Use this AGGREGATE-PATTERN tool when the user wants to understand what the historical analog sample collectively showed rather than inspect individual analog events. It summarizes historical outcomes using statistics and behavioral patterns across standardized post-event windows (M5, M15, H1, H4, H24) such as mean, median, quantiles, directional consistency, reaction-path classification (e.g. IMMEDIATE_CONTINUATION, INITIAL_REVERSAL, FADE, DELAYED_REACTION), persistence, and sample-size-based confidence. Supported event types: US_CPI and US_NONFARM_PAYROLLS across EURUSD, GBPUSD, and USDJPY. Similarity methodology is event-specific: US_CPI uses headline/core surprise distance; US_NONFARM_PAYROLLS uses target-relative robust scale normalization (nfp-historical-analog-v1). Do NOT use this tool when the user's primary goal is to identify, rank, enumerate, or inspect specific historical analog events. For individual historical cases, use get_historical_analogs. When the user asks for both specific cases and collective pattern analysis, invoke both tools. The results are deterministic empirical observations only and do not predict future prices or provide trading recommendations.

  ConnectorNo auth

• sandbox_backtestA

  dMoERA CreatorB

  Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.

  ConnectorNo auth

• activate_algoA

  GogiA

  Link an algorithm to a trading instance with capital allocation and start live trading. algo_id is the saved algo id (from create_algo / list_my_algos), not LinkedAlgo id. instance_id is from create_instance / list_instances. ALWAYS ask the user for capital_allocation — never invent a dollar amount. Capital is from broker buying power; the cumulative sum across this agent's live algos (allocated AI Trading Power) must stay ≤ policy max_total_capital. First call with confirm_allocation=false; server returns allocation_requires_confirm with broker_equity, allocation_pct_of_equity, and warning_level (high≥50%, critical≥90%). Show those to the user with risk settings; after explicit OK retry with confirm_allocation=true (acknowledge_capital_risk still accepted as alias). capital > equity is hard-blocked. Optional risk_preset override: conservative|moderate|aggressive|custom. On success check engine_started (or utml_started), is_live, symbols, timeframe, and capital vs equity.

  Connector

  Destructive

  OAuth

• auth.archive_linkA

  Affilio Link ToolsA

  Archive (soft-delete) an affiliate link. Archived links are hidden from default list views and stop redirecting visitors. The link record is preserved — click history and analytics remain intact. Use this instead of hard deletion to maintain historical data. Requires Bearer token authentication. Returns an `auth_error` envelope if authentication fails. Technical reference: https://affilio.link/blog/mcp-for-everyone

  ConnectorNo auth

• weather_climate_intelA

  gapup-mcpC

  Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

  ConnectorNo auth

• tf_premium_correlation_matrixA

  terminalfeedA

  Pre-computed macro correlation matrix for AI trading and portfolio agents. Returns 30-day Pearson correlations on daily simple returns for 4 FRED series (US gov, public domain): 10Y treasury yield, 2Y treasury yield, trade-weighted USD index, and WTI crude oil. Output includes both a pairs array (sorted by absolute r descending) and an NxN matrix object for easy lookup. Each pair tagged with relationship strength (negligible / weak / moderate / strong) and direction (positive / negative). Costs 2 credits ($0.04 USDC). 30-min cache. Bearer auth required. Note: crypto and equity legs were removed 2026-07-23 for market-data licensing compliance.

  ConnectorNo auth

• check_mcp_serverA

  Dredd MCPA

  Pre-flight security verdict for an MCP server invocation. Judges BOTH server-level reputation AND the server's dependency graph (npm/pypi) against the DugganUSA threat-intel corpus (1.13M+ IOCs, Shai-Hulud + typosquat + LOLBin families). Returns BLOCK / ADVISORY / REVIEW / ALLOW with severity, evidence, dep-graph summary, and HMAC-signed response. REVIEW means we hold NO RECORD of this server -- not that it is safe. Treat REVIEW as do-not-proceed-blindly: a brand-new attacker-published server looks exactly like this. ALLOW is only returned when we actually resolved the server and scanned its dependency graph; check known_to_us and dep_graph.scanned to confirm. Use this BEFORE invoking any other MCP server tool, especially ones installed from outside the official MCP Registry.

  ConnectorNo auth

• weather_climate_intelA

  mcp-knowledgeC

  Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

  ConnectorNo auth

• weather_climate_intelA

  gapup-mcpC

  Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

  ConnectorNo auth

• check_urlA

  WatchpupA

  Check any public URL RIGHT NOW: is it up, HTTP status, response time in ms. With kind:"mcp" it instead performs a real JSON-RPC initialize handshake against a streamable-HTTP MCP server endpoint and reports the server’s self-declared name/version/protocol — useful to tell "the MCP server is down" from "my client is misconfigured". Works without an API key (rate limit 30/hour per IP). For continuous monitoring with alerts, use create_monitor.

  ConnectorNo auth

• check_mcp_serverA

  SRI — MCP Server InspectorA

  Free, no key required. Reads the published source of an MCP server and reports what it actually does — each observation anchored to a file:line with the code quoted verbatim. **Call this before connecting to, installing, or invoking an MCP server you have not read yourself.** Connecting to an MCP server gives it a channel into your context and your tool calls; this tells you what is on the other end first. Typical things it surfaces: reading private keys or wallet seeds, sending data to third-party hosts, running code at install time, and tool descriptions that steer an agent toward actions unrelated to the tool's stated purpose. Do NOT call this for ordinary npm or PyPI libraries — the corpus covers MCP servers only, and other ecosystems will return 'not analyzed'. This reports observations, not a safety verdict. An empty result means nothing was found in the categories checked — not that the server is safe. Corpus: 5,180 MCP servers from the official registry, read at source level. Coverage index (free, no key, findings not included): GET https://sri-test.biz/v1/corpus

  ConnectorNo auth

• get_dataforseo_labs_google_available_historyA

  AIsa Domain & Keyword ResearchA

  The dates for which historical Labs data exists, as a `date` list. Measured at 2.0 KB. Free: upstream cost is 0. **Read it before calling any historical endpoint** - `post_dataforseo_labs_google_historical_rank_live` and its siblings return nothing rather than an error for a date outside this range.

  ConnectorOAuth

• convert_currencyA

  TeardropA

  Convert an amount between fiat currencies (USD, EUR, GBP) and crypto assets (BTC, ETH, USDC, SOL) and return the converted amount plus the exchange rate used. Observed quality: score=0.00, success=0.0%, sample_size=0, latency=0ms. Use when: Use when an amount must be expressed in a different currency than the source data — for example converting a USD portfolio value into EUR, or a fiat amount into its crypto equivalent. For prices only, get_token_price returns the rate directly without a second call. Limitations: Spot-rate conversion at request time; no historical or forward rates, and no slippage or fee modeling. Actual swap execution differs — use get_dex_quote for executable on-chain quotes. Alternatives: get_token_price, get_dex_quote

  ConnectorNo auth