"Information on Formula 1" matching MCP tools:

• add_form_fieldsA

  FaldaroA

  Add new fields to a form’s draft without touching anything else — safe on forms of any size, because the schema is read and written back server-side. Each field needs at least slug (unique on the form), type and label; optional properties (required, visible, formula, options, placeholder, helpText, min, max…) pass through. For TABLE and LIST fields, columns is an array of plain heading STRINGS (e.g. ["Make", "Value"]) — the headings double as the row keys submissions store; never objects. Placement: `page` is the 1-based page number (default: last page); `afterSlug` inserts after an existing field on that page (default: end of the page). To change existing fields use patch_form_fields; to delete or reorder them use remove_form_fields and move_form_field. Requires form:write.

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• compare_analytical_vs_simulatedA

  QueueSimA

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

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• lora_airtimeA

  calc.engineerA

  Calculates LoRa packet time-on-air using the Semtech AN1200.13 formula. Computes symbol duration, preamble time, payload symbol count, effective data rate, and the minimum transmission interval for 1% duty cycle compliance. Essential for capacity planning in LoRaWAN and Meshtastic mesh networks. Accepts spreading factor (SF7-SF12), bandwidth (125/250/500 kHz), coding rate (4/5-4/8), payload size, header mode, CRC, and optional low data rate optimization. Feeds airtime_ms to channel_utilization for mesh load analysis.

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• calculate_fraction_arithmeticA

  CalcgridB

  Use this when you need an exact result of an operation on two fractions (e.g. 3/4 + 1/6), including mixed numbers such as 1 1/2, rather than a rounded decimal. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when you only want to reduce a single fraction or convert it to a decimal or percent (use fraction-simplifier), or you need the least common denominator of several denominators (use gcd-lcm). What it computes: Adds, subtracts, multiplies or divides two fractions, mixed numbers, whole numbers or decimals with exact integer arithmetic, returning the result in lowest terms, as a mixed number and as a decimal, with the intermediate steps. Inputs: fraction_a (string); fraction_b (string); operation (enum, optional). Complete JSON argument examples: {"fraction_a":"3/4","fraction_b":"1/6","operation":"add"} | {"fraction_a":"2 1/3","fraction_b":"5/6","operation":"subtract"} Outputs: result, mixed_number, numerator, denominator, decimal, working. Formula: a/b + c/d = (a·(L/b) + c·(L/d)) / L with L = lcm(b, d), a/b − c/d likewise; a/b × c/d = (a·c) / (b·d); a/b ÷ c/d = (a·d) / (b·c); the result is divided by gcd(numerator, denominator) Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/fraction-arithmetic with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/math/fraction-arithmetic.md

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• calculate_ab_test_significanceA

  CalcgridB

  Use this when you have visitors and conversions for a control and a variant (A/B test, split test, email test) and need to know whether the difference is statistically significant. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when the metric is a mean (revenue per user, time on page) rather than a conversion proportion, or there are more than two variants (use a chi-square or multiple-comparison procedure). What it computes: Tests whether a variant's conversion rate differs from the control's using the pooled two-proportion z-test, and reports both rates, absolute and relative lift, z-score, two-sided p-value, a confidence interval for the difference and the sample size needed per variant to detect the observed lift with 80 % power. Inputs: control_visitors (integer); control_conversions (integer); variant_visitors (integer); variant_conversions (integer); confidence_level_percent (number, %, optional). Complete JSON argument examples: {"control_visitors":1000,"control_conversions":50,"variant_visitors":1000,"variant_conversions":70} | {"control_visitors":5000,"control_conversions":200,"variant_visitors":5000,"variant_conversions":260} Outputs: control_rate_percent [%], variant_rate_percent [%], absolute_lift_percentage_points [pp], relative_lift_percent [%], standard_error_percentage_points [pp], z_score, p_value, significant, ci_lower_percentage_points [pp], ci_upper_percentage_points [pp], required_visitors_per_variant, verdict. Formula: p1 = x1/n1, p2 = x2/n2, p̄ = (x1 + x2)/(n1 + n2); z = (p2 − p1) / √(p̄(1 − p̄)(1/n1 + 1/n2)); p_value = 2·(1 − Φ(|z|)); CI = (p2 − p1) ± z_crit·√(p1(1 − p1)/n1 + p2(1 − p2)/n2); n per variant = (z_crit·√(2·p̄(1 − p̄)) + z_0.80·√(p1(1 − p1) + p2(1 − p2)))² / (p2 − p1)² Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/ab-test-significance with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/business/ab-test-significance.md

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• convert_betting_oddsA

  CalcgridB

  Use this when you have odds in one format and need the other two, the implied probability of a price, or the payout of a stake at those odds. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when you need the combined price of several selections (use parlay-odds), the value of a bet given your own probability estimate (use bet-expected-value), or a bookmaker's margin across a whole market (use arbitrage-bet). Mathematics only; not gambling advice. What it computes: Converts betting odds between American (moneyline), decimal (European) and fractional (UK) formats, auto-detecting the input format, and reports the implied probability, profit and total return for a stake. Inputs: odds (string); format (enum, optional); stake (number, optional). Complete JSON argument examples: {"odds":"+150"} | {"odds":"-200","format":"auto","stake":100} Outputs: american, decimal, fractional, implied_probability_percent [%], profit_on_stake, total_return, detected_format. Formula: decimal = 1 + american/100 (american > 0) or 1 + 100/|american| (american < 0); decimal = 1 + a/b (fractional a/b); american = +100·(decimal − 1) if decimal ≥ 2, else −100/(decimal − 1); fractional = (decimal − 1) reduced with the GCD; implied_probability_percent = 100/decimal; total_return = stake × decimal Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/odds-converter with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/gaming/odds-converter.md

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• varrd_aiA

  VARRD — Statistically Validated Trading Edges + AI Research EngineA

  Talk to VARRD AI (~$0.25/turn). Describe any trading idea in plain language and the system handles everything — loading decades of market data, charting your pattern, running statistical tests, backtesting with stops, and generating exact trade setups. MULTI-TURN: First call creates a session. Keep calling with the same session_id, following context.next_actions each time. 1. Your idea -> VARRD charts pattern 2. 'test it' -> statistical test (event study or backtest) 3. 'show me the trade setup' -> exact entry/stop/target prices HYPOTHESIS INTEGRITY (critical): VARRD tests ONE hypothesis at a time — one formula, one setup. Never combine multiple setups into one formula or ask to 'test all' — each idea must be tested as a separate hypothesis for the statistics to be valid. Say 'start a new hypothesis' between ideas to reset cleanly. - ALLOWED: Test the SAME setup across multiple markets ('test this on ES, NQ, and CL') — same formula, different data. - NOT ALLOWED: Test multiple DIFFERENT formulas/setups at once — each is a separate hypothesis requiring its own chart-test-result cycle. If ELROND council returns 4 setups, test each one separately: chart setup 1 -> test -> results -> 'start new hypothesis' -> chart setup 2 -> etc. KEY CAPABILITIES you can ask for: - 'Use the ELROND council on [market]' -> 8 expert investigators - 'Optimize the stop loss and take profit' -> SL/TP grid search - 'Test this on ES, NQ, and CL' -> multi-market testing - 'Simulate trading this with 1.5 ATR stop' -> backtest with stops EDGE VERDICTS in context.edge_verdict after testing: - STRONG EDGE: Significant vs zero AND vs market baseline - MARGINAL: Significant vs zero only (beats nothing, but real signal) - PINNED: Significant vs market only (flat returns but different from market) - NO EDGE: Neither significant test passed TERMINAL STATES: Stop when context.has_edge is true (edge found) or false (no edge — valid result). Always read context.next_actions.

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• validate_formulaA

  DockA

  Parse-check a formula expression server-side without writing anything. Returns { ok, error?, rewrittenFormula?, referencedFunctions, unknownFunctions }. Use BEFORE update_row / create_row when the formula references functions or syntax you're not 100% sure of: a `=SUMIFS(...)` with the wrong arg order or a misspelled `=AVERAG(...)` will round-trip into the cell as a stored carrier with no value, and the user will see #NAME? or #VALUE? on next view. Catch it here. `unknownFunctions` flags any identifier that isn't in the Dock Sheets catalog (including likely typos); `referencedFunctions` lists the canonical post-alias names the engine will see. Cheap, public, no auth, no workspace context needed.

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• evaluate_formulaA

  DockA

  Evaluate a formula expression against an actual Dock workspace's columns + rows, server-side, returning the same display value the UI's HyperFormula engine would render. Two modes: STANDALONE (omit `workspace_slug`) — evaluates against an empty grid; useful for `=SUM(1, 2, 3)` or any formula with no cell references. IN-WORKSPACE (pass `workspace_slug`, optionally `at`) — loads the workspace's grid, evaluates the formula as if pasted into the `at` cell (or A1 if omitted), resolves real refs against actual data. Returns { ok, displayValue, error? }. Workspace mode requires read access; standalone mode is public.

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• calculate_unlever_betaA

  valuation-apiA

  Unlever a (levered) equity beta to its asset beta using the Hamada formula — removing the financial-risk effect of debt so betas of companies with different capital structures can be compared. Formula: Beta(unlevered) = Beta(levered) / (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use when valuing a private company or a deal with a different capital structure than the public comparable — unlever the comps’ betas, average them, then relever at your target structure. WHEN NOT TO USE: Do NOT unlever with an inconsistent tax rate or debt/equity ratio — the result is only as clean as its inputs; for companies with significant non-debt liabilities consider a more advanced formula. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { unlevered_beta: number (e.g. 0.85), inputs }. PARAMETERS: levered_beta (required): The observed (levered) equity beta of the comparable company, e.g. 1.2. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Debt-to-equity ratio of the company whose beta is being unlevered (market values preferred), e.g. 0.5 = 0.5x. Must be >= 0.

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• calculate_relever_betaA

  valuation-apiA

  Relever an unlevered (asset) beta to a target capital structure using the Hamada formula — restoring financial risk for the specific debt/equity mix of the company or deal being valued. Formula: Beta(levered) = Beta(unlevered) x (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use AFTER unlevering comparable betas: apply the average unlevered beta to your target company’s (or transaction’s) capital structure to obtain the beta for WACC. WHEN NOT TO USE: Do NOT relever onto an unrealistic target structure — extreme leverage produces extreme betas that may overstate risk; sanity-check the resulting cost of equity. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { levered_beta: number (e.g. 1.15), inputs }. PARAMETERS: unlevered_beta (required): Unlevered (asset) beta, e.g. 0.85. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Target debt-to-equity ratio (market values preferred), e.g. 0.6 = 0.6x. Must be >= 0.

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• calculate_unlever_betaA

  valuation-apiA

  Unlever a (levered) equity beta to its asset beta using the Hamada formula — removing the financial-risk effect of debt so betas of companies with different capital structures can be compared. Formula: Beta(unlevered) = Beta(levered) / (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use when valuing a private company or a deal with a different capital structure than the public comparable — unlever the comps’ betas, average them, then relever at your target structure. WHEN NOT TO USE: Do NOT unlever with an inconsistent tax rate or debt/equity ratio — the result is only as clean as its inputs; for companies with significant non-debt liabilities consider a more advanced formula. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { unlevered_beta: number (e.g. 0.85), inputs }. PARAMETERS: levered_beta (required): The observed (levered) equity beta of the comparable company, e.g. 1.2. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Debt-to-equity ratio of the company whose beta is being unlevered (market values preferred), e.g. 0.5 = 0.5x. Must be >= 0.

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• calculate_relever_betaA

  valuation-apiA

  Relever an unlevered (asset) beta to a target capital structure using the Hamada formula — restoring financial risk for the specific debt/equity mix of the company or deal being valued. Formula: Beta(levered) = Beta(unlevered) x (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use AFTER unlevering comparable betas: apply the average unlevered beta to your target company’s (or transaction’s) capital structure to obtain the beta for WACC. WHEN NOT TO USE: Do NOT relever onto an unrealistic target structure — extreme leverage produces extreme betas that may overstate risk; sanity-check the resulting cost of equity. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { levered_beta: number (e.g. 1.15), inputs }. PARAMETERS: unlevered_beta (required): Unlevered (asset) beta, e.g. 0.85. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Target debt-to-equity ratio (market values preferred), e.g. 0.6 = 0.6x. Must be >= 0.

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• calculate_unlever_betaA

  Valuation APIA

  Unlever a (levered) equity beta to its asset beta using the Hamada formula — removing the financial-risk effect of debt so betas of companies with different capital structures can be compared. Formula: Beta(unlevered) = Beta(levered) / (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use when valuing a private company or a deal with a different capital structure than the public comparable — unlever the comps’ betas, average them, then relever at your target structure. WHEN NOT TO USE: Do NOT unlever with an inconsistent tax rate or debt/equity ratio — the result is only as clean as its inputs; for companies with significant non-debt liabilities consider a more advanced formula. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { unlevered_beta: number (e.g. 0.85), inputs }. PARAMETERS: levered_beta (required): The observed (levered) equity beta of the comparable company, e.g. 1.2. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Debt-to-equity ratio of the company whose beta is being unlevered (market values preferred), e.g. 0.5 = 0.5x. Must be >= 0.

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• calculate_relever_betaA

  Valuation APIA

  Relever an unlevered (asset) beta to a target capital structure using the Hamada formula — restoring financial risk for the specific debt/equity mix of the company or deal being valued. Formula: Beta(levered) = Beta(unlevered) x (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use AFTER unlevering comparable betas: apply the average unlevered beta to your target company’s (or transaction’s) capital structure to obtain the beta for WACC. WHEN NOT TO USE: Do NOT relever onto an unrealistic target structure — extreme leverage produces extreme betas that may overstate risk; sanity-check the resulting cost of equity. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { levered_beta: number (e.g. 1.15), inputs }. PARAMETERS: unlevered_beta (required): Unlevered (asset) beta, e.g. 0.85. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Target debt-to-equity ratio (market values preferred), e.g. 0.6 = 0.6x. Must be >= 0.

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• canon_list_formulasA

  Canon (wallstreet.wiki)A

  The deterministic formula registry: id, site, summary, input names and citations. Call canon_describe_formula for a schema.

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• f1.standings.driversA

  apibaseB

  Get Formula 1 World Drivers Championship standings for any season. Returns each driver ranked by points, with their constructor, nationality, wins count, and total championship points as of the latest completed round. Omit the season parameter to get current live standings. Historical seasons available back to 1950. Data source: Jolpica F1 (Ergast-compatible API) — free, unlimited, no auth required.

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• add_ireland_working_daysA

  CalcgridB

  Use this when you need a deterministic Ireland national public-holiday offset. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when you need an employer-selected benefit day or non-national schedule. What it computes: Adds or subtracts Ireland national working days in exact 2026-2027 coverage. Example user requests: Add one Ireland working day after 2027-01-29. Inputs: start_date (date); working_days (integer). Complete JSON argument examples: {"start_date":"2027-01-29","working_days":1} Outputs: start_date, result_date, working_days_added, calendar_days_elapsed, public_holidays_skipped, territory, territory_name, holiday_names, calendar_scope, good_friday_policy, weekend_holiday_policy, calendar_profile, dataset_version, sources. Formula: move one day at a time; decrement only on Ireland status.is_working_day=true Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/ireland-add-working-days with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/everyday/ireland-add-working-days.md

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• sip_investmentA

  TrueCalciA

  Calculate Systematic Investment Plan (SIP) mutual fund maturity wealth with optional annual percentage step-up (top-up) for compounding wealth growth. Behavior: Deterministic, idempotent calculation with zero external side effects. Models monthly SIP compounding using formula FV = P * [((1 + i)^n - 1) / i] * (1 + i). When stepUpPercent > 0, dynamically increases monthly installment each 12-month cycle. Returns maturity amount, total invested capital, total estimated capital gains, and year-by-year accumulation. Usage Guidelines: Use for mutual fund SIP investments, recurring deposits, and goal-based financial planning. Do not use for US 401(k) / Roth IRA compounding with lump sum; use compound_wealth instead.

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• ios_mjpeg_screenshotA

  Robot Actions — Remote Device ControlB

  PREFERRED fast screenshot of an iOS device via the MJPEG stream. Returns the JPEG image PLUS a text block with the image pixel size and the device POINT size, and the exact formula to convert a pixel you read off the image into iOS screen points for the HID/tap tools (the image is NOT 1:1 with device points — guessing that scale is the #1 cause of missed taps). Much faster than ios_screenshot; use it for routine per-action checks. Auto-wakes on a black frame. Requires an active iOS automation session, because the speed comes from reading the frame stream that session is already running — with no session, use ios_fast_screenshot instead.

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• workflow.run_forex_correlationA

  TradingCalc MCP: Options, Forex, Risk Stats, Prediction Markets, On-Chain & Crypto FuturesA

  Correlation coefficient and minimum-variance hedge ratio between two price series of matching length, computed on daily % returns (not raw price levels, which would give spuriously high correlation between two unrelated but both-trending series). hedgeRatio follows Hull's standard futures-hedging formula: cov(returns1,returns2)/var(returns2), "how many units of series 2 per unit of series 1 minimizes the combined position's variance." No live data fetch: supply the two price series directly. The live variant is workflow.run_forex_correlation_live, which has both series fetched automatically for two named pairs. Use when user already has two price series and wants their statistical relationship. Returns: n, correlation (-1 to 1), hedgeRatio.

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