"Improving OKR Strategies" matching MCP tools:

• analytics_aggregate_entitiesA

  ZentrikB

  Aggregate workspace entity data with SQL for counts, rankings, volumes, distributions, and trends. Use this tool instead of list/search tools whenever the user asks "which/top/most/how many/volume/by product/by account/by month". Use for examples like: "Which accounts have the most signals in the last 90 days?" -> entity signal, groupBy account, metric count, filters.timeWindow 90d. Use for examples like: "Show insight volume by product this month" -> entity insight, groupBy product, metric count, filters.since = start of current month. Use for examples like: "Top ideas by score" -> entity idea, groupBy account/product/status/month as appropriate, metric avg(globalScore) only when grouping ideas. Use for opportunity strategy questions like: "Where is impact concentrated across the roadmap?" -> entity opportunity, groupBy product (or okr), metric avg(expansionScore); "Which objectives have the most opportunities behind them?" -> entity opportunity, groupBy okr, metric count. avg(expansionScore) and groupBy okr are opportunity-only. Use for idea-vote questions like: "Which ideas have the most votes?" -> entity idea_vote, groupBy idea, metric count; "Which accounts voted most this quarter?" -> entity idea_vote, groupBy account, metric count, filters.since = quarter start. For taxonomy questions, call discovery.describe_taxonomy first and use its group key; never guess one. Examples: "How many insights are native-gap by product area?" -> entity insight, groupBy taxonomy; "How many ideas are in each Stage, Category, or PHI level?" -> entity idea, groupBy taxonomy. Set taxonomyGroupKey to the discovered workspace group key and metric to count, sum(arr), or distinct(account). List, multi-select, text, and unassigned values are returned in the workspace's own vocabulary. Validated capability matrix: signal: groups [account, status, type, month], metrics [count, sum(arr), distinct(account)], filters [status, type, accountId, accountIds]; insight: groups [account, product, tag, status, priority, type, month], metrics [count, sum(arr), distinct(account)], filters [status, priority, type, productId, accountId, accountIds, tagId]; opportunity: groups [account, product, tag, status, priority, month, okr], metrics [count, sum(arr), avg(expansionScore), distinct(account)], filters [status, priority, productId, accountId, accountIds, tagId]; idea: groups [account, product, tag, status, priority, month], metrics [count, sum(arr), avg(globalScore), distinct(account)], filters [status, priority, productId, accountId, accountIds, tagId]; idea_vote: groups [idea, account, product, tag, status, priority, month], metrics [count, sum(arr), distinct(account)], filters [status, priority, productId, accountId, accountIds, tagId]; initiative: groups [product, tag, status, priority, month], metrics [count], filters [status, priority, productId, tagId]; account: groups [account, status, type, month], metrics [count, sum(arr), distinct(account)], filters [status, type, accountId, accountIds]. All entities also accept timeWindow or since. Taxonomy grouping supports insight and idea with count, sum(arr), and distinct(account). Do not fetch raw insights/signals/accounts/ideas and count them in model context when this tool can compute the aggregate.

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• verify_revenue_recoveryA

  Woo Revenue Incident GraphA

  Answers: after a change was made, did this store's revenue signals actually recover? Compares the window before a stated change against the window since: paid vs pending vs failed order mix, webhook failure counts, and gateway availability. Returns `resolved`, `improving`, `still_failing`, or `unknown` — and `unknown` when too little new data has arrived to tell, which is free. A quiet ten minutes is not a recovery. Free while in beta.

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• verify_revenue_recoveryA

  Woo Revenue Incident GraphA

  Answers: after a change was made, did this store's revenue signals actually recover? Compares the window before a stated change against the window since: paid vs pending vs failed order mix, webhook failure counts, and gateway availability. Returns `resolved`, `improving`, `still_failing`, or `unknown` — and `unknown` when too little new data has arrived to tell, which is free. A quiet ten minutes is not a recovery. Free while in beta.

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• trend_analysisA

  Hermes — Air Quality IntelligenceA

  Analyse the long-term trend in a pollutant near a location. Uses Theil-Sen slope estimation with Mann-Kendall significance testing to determine whether air quality is improving, worsening, or stable. Robust to outliers and missing data. Returns a 'summary' with plain-English trend description and statistical details. Present the summary to users first. Args: location: Postcode, place name, or "lat,lon". pollutant: Pollutant to analyse — "NO2", "PM2.5", "PM10", "O3" (default "NO2"). years: Number of years of data to analyse (default 5, range 2–5). Requests outside this range are clamped; the response includes ``metadata.years_clamped`` and a note in ``summary`` when so.

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• flipr_opportunityA

  flipr-x402A

  Analyze current Flipr.bet opportunity before deciding to flip. Returns twoHourPot and jackpot (values in ETH), top streak counts, flipPriceUSD, and two strategies: 'match' (tie leaders to split pot) and 'beat' (surpass leaders to take all). Each strategy shows expected cost in ETH, number of flips needed, and ROI. ROI > 1.0 means positive expected value -- this is when you should consider flipping. Pots are in ETH; flip cost is paid in USDC via x402. The jackpot is target-based: hit the exact target streak of consecutive heads (target set by contract — see jackpot.targetStreak in this response) to win 80% of the jackpot pot. Funded by a portion of flip fees. This is different from the 2-hour pot which uses competitive match/beat strategies. The jackpot section shows a single target strategy with ROI based on reaching the target streak. Call this FIRST before using flipr_flip. FREE — rate-limited only. [pricing: {"cost":"0","currency":"FREE","type":"free","network":"eip155:8453"}]

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• compare_backtestsA

  Backtest360A

  Run several strategies on the same data and compare side by side. One quota-counted call, but compute scales with the number of strategies. If the wall-clock compute budget is exceeded, the call fails with a tool error (504) instead of returning partial results — narrow the request (fewer strategies, shorter date range, coarser frequency) and retry. Args: data_source: Shared data source (same shape as run_backtest). strategies: List of {"label": str, "strategy": {...}, "execution": {...}?} entries. Labels need not be unique or id-safe — they are echoed back verbatim in the result. include_benchmark: Add a buy-and-hold benchmark to the comparison. response_detail: Shaping level applied to each strategy's result. trades_limit: Max trades per strategy when detail is 'full'. Returns: {"strategies": [{"label", "result"}, ...], "equity_curves": {...}, "alignment"?}, each result shaped at the requested detail. When a benchmark is included, non-benchmark entries also carry "relative" (beta, alpha, information ratio, etc.). A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error.

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• arena_compare_strategiesA

  Backtesting ArenaA

  Which of these strategies performed best on the same data? Run 2–5 strategies against the SAME pair, interval and date range and return per-strategy metrics plus a comparison summary (best by CAGR, best by win-rate, worst by drawdown). Use this when the user asks which of several strategies fits a market — it holds the pair, interval and requested date range fixed, which a series of separate arena_run_backtest calls does not guarantee. What it does NOT equalize is the EVALUATION window: a strategy with a long warmup starts trading later, so compare actual_date_from across the runs and check result.benchmark before ranking by CAGR. For one strategy across many pairs use arena_run_universe_backtest instead. Caveat worth passing on: comparing N strategies and reporting the winner IS multiple testing — the winner’s edge is upward-biased. arena_get_robustness_field puts a counted N on that. Sequential, expect 10–50s. Per-day quota: Pro=20, Power=200. [API Pro tier]

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• get_briefedA

  VARRD — Statistically Validated Trading Edges + AI Research EngineA

  Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book. Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge'). Requires at least 5 strong edges in your library. Costs credits.

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• beycome_estimateA

  listingA

  Discovery stage — estimate a property's SALE value (beycome CMA + Zillow Zestimate). No account or prop_id needed. Returns both estimates plus suggested pricing strategies (fast / balanced / max). Use during discovery to set a list price; pair with `beycome_comps`. For rental inquiries, use `beycome_rental_estimate` instead.

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• arena_get_strategy_insightsA

  Backtesting ArenaA

  Which strategy and interval combinations actually performed? Aggregated backtest performance per (strategy × interval) cell. If `strategy` AND `interval` provided, returns detail with per-asset breakdown + param variants. Otherwise returns the matrix. Free tier is limited to the same strategies that are free in the backtester itself (rsi_sma, golden_cross, rsi_ob_os, bnh_fixed, dca_reference); the response then carries `plan_capped: true` plus `plan_cap_note`, so a short matrix is never mistaken for a thin database. Detail mode on a Pro-only strategy returns 403 rather than a silently empty answer. API Pro and Power receive every cell. [Free: 5 strategies / Pro+: full]

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• best_useA

  Real Estate AnalyzerA

  Screen ONE property across every strategy the provided inputs qualify for and rank them by deal score — answers "what is the best use of this property?". Provide a superset of inputs (price, marketRent, adr, occupancy, rehabBudget, arv, units, …); strategies missing inputs are skipped with reasons. All rates/percents are FRACTIONS (0.0675 = 6.75%). Omitted operating inputs are filled with documented defaults and listed in assumptions.estimated_fields. Free, no key.

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• find_recoveringA

  DataPulseA

  Return datasets whose published freshness trend is recovering, with the fastest staleness reductions first. Includes pipeline-computed trend and publish-reliability evidence. Use it for improving freshness trends; do not use it for deterioration, anomalies, reliability grades, or structural drift—use find_deteriorating, find_anomalies, find_unreliable, or find_schema_drift instead. It reads precomputed trend data, so an empty result means no published recovering row exists; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.

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• get_track_recordA

  Liquid State — Crypto Market IntelligenceA

  Liquid State's public paper-strategy track record — deterministic, rule-based strategies with return %, max drawdown %, and days live for each, updated daily. Paper, not live capital; losses are never hidden. Use when the user asks whether Liquid State's calls or strategies actually work, or wants a verifiable track record rather than a claim.

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• fork_shared_portfolioA

  NexusTrade Financial MCPA

  ⚠ MUTATING — creates or modifies a portfolio. Fork strategies from a shared portfolio into the user's account. target: 'new' creates a chat portfolio; 'existing' patches a deployed portfolio. mode: 'replace' (default) removes old strategies, 'append' keeps them. For monetized portfolios, subscribe first. Returns { forkSharedPortfolioResult: { portfolioId, name, addedCount, removedCount, ... } }. Prefer fork when the user wants to edit/customize strategies.

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• catalog_searchA

  BasisA

  Search what Basis can do: indicators, formula functions, built-in strategies, the extra series available inside strategy expressions, and the drawing tools with the number of anchors each one takes. Exact counts are in the system prompt catalogue. Use this before assuming something does or does not exist — and always before drawing, because a drawing tool id you guessed is refused. Leave the query empty to list everything of a kind.

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• get_strategy_leaderboardA

  OneQAZ Trading IntelligenceA

  Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edge surface for trust auditing. Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?", "top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?". When to call: final trust-validation step. Prerequisites: none. Next steps: market://{market_id}/signals/summary for live signals. Caveats: `min_trades` filter enforces statistical validity. Strategies are paper-tested, not real-money executed.

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• get_top_trading_strategiesA

  Helium MCP Server - News, Markets & AIA

  Get the top-ranked short volatility and long volatility option trading strategies. Returns two ranked lists — short_volatility (sell premium / theta strategies) and long_volatility (buy premium / gamma strategies) — each containing up to `limit` tickers. Each entry has the same fields as get_ticker: - ticker, name, latest_price, page_url - bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated, when available) - price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (when available) - iv_rank_percentile (0-100, IV rank over past year, when available) - short_vol_call, short_vol_put: best short volatility option packs (when available) - long_vol_call, long_vol_put: best long volatility option packs (when available) Sort options: - "helium_rank" (default): Helium AI edge score — best overall expected value - "odds_of_profit": Highest probability of profit - "historical_performance": Best annualized historical P&L across backtested trades - "reward_to_risk": Best reward-to-risk ratio - "smallest_max_loss": Strategies with the smallest maximum possible loss Args: sort: Ranking method (default "helium_rank"). One of: 'helium_rank', 'odds_of_profit', 'historical_performance', 'reward_to_risk', 'smallest_max_loss'. limit: Number of results per strategy type (1-20, default 5).

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• export_backtestA

  Backtest360A

  Export a multi-strategy comparison as an Excel workbook. Quota-counted; needs a key whose plan includes full-metrics export (a 403 means the configured key's plan does not — do not retry). Returns the workbook base64-encoded — decode and write it to a ``.xlsx`` file. Args: data_source: Shared data source (same shape as run_backtest). strategies: Same shape as compare_backtests' ``strategies``. include_benchmark: Add a buy-and-hold benchmark to the export. Returns: {"filename", "content_type", "size_bytes", "content_base64"}. A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error. If the encoded workbook would exceed the output size limit, raises a tool error — narrow the request (shorter date range, fewer strategies, coarser frequency) and retry.

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• suggest_improvementsA

  SigRank — AI Operator BenchmarkingB

  Generates ranked, simulated improvement suggestions for your token cascade. Takes 4 token pillars, tests multiple strategies (increase cache reads, reduce input, increase output, optimize cache creation), simulates each, and returns them ranked by Υ yield impact. Each suggestion includes the action, pillar to change, projected Υ, yield delta, projected class, and rationale. Returns the single highest-impact change as best_single_change.

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• get_security_scoreA

  RoutebaseA

  Get the active project's security score (0–100) and the count of open findings by severity. Pass trendDays to also get the daily score history over that many days — that is how you tell whether a project is improving or drifting. Use this for the score and open counts; for the findings behind it use get_security_findings. Requires project context.

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• list_recovery_strategiesA

  Wellness ProjectA

  List the user's recovery and mindfulness strategies. Use when the user asks about their recovery practices, mindfulness routines, or you need strategy IDs before logging a session. INFER — do not ask: - filter: default to 'active'; use 'all' for history; use 'historical' for ended strategies only. Returns each strategy's id, name, category, schedule, start_date, and end_date.

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