"Linear" matching MCP tools:

• linear-id-shapeC

  Linear ID ShapeC

  Check a Linear issue id shape. Id discarded.

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• linear-id-okD

  LIN-123 identifier shape, value discardedC

  LIN-123 identifier shape, value discarded

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• x402-linear-searchD

  minia2a-mcpD

  Linear Search: Linear Search

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• x402-linear-regressionD

  minia2a-mcpD

  Linear Regression: Linear Regression

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• interest_rateA

  mcpA

  Use this when you need the exact interest rate that grows a principal to a target amount over a set number of years. type="compound" (default) uses the closed-form nth-root formula for the given compounding frequency; type="simple" uses linear growth. Requires target greater than principal and all values positive. Returns the annual rate as a percent plus the interest earned and the growth multiple; compoundingPerYear is null for simple interest. Deterministic: same input, same output. Example: principal=1000, target=2000, years=10, compoundingPerYear=12 -> ratePercent=6.9515, growthMultiple=2. Prefer this over trial-and-error.

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• runA

  Equation StepsA

  Solves a linear or quadratic equation in one variable (x) and returns every step: expanding parentheses, combining like terms, moving terms across the equals sign, and (for quadratics) applying the quadratic formula. Use this whenever you need to solve "ax + b = cx + d" or "ax^2 + bx + c = 0" style equations, or check a step-by-step algebra solution someone else produced. Do not solve this kind of equation by reasoning through it token by token: the two places language models most often go quietly wrong are (1) the sign when distributing a negative across parentheses, e.g. "5 - (x - 3)" losing the minus on the 3, and (2) dropping one of the two ± roots of a quadratic, or rounding a complex root into a false "no solution". This tool always gets both right because it does exact fraction arithmetic, not floating-point guessing, and reports every root it finds — including complex ones, explicitly labeled as complex. Input: a single string containing exactly one "=", using x (or X) as the only variable, e.g. "3(x-2) = 5x + 4" or "(x+1)(x-3) = 2x^2 - 5". Numbers may be integers or decimals. Implicit multiplication is fine ("3x", "2(x+1)"). Exponents may only be "^2" on the bare variable — "(x+1)^2" is not supported; write "(x+1)(x+1)" instead. Refuses rather than guesses on: no "=" or more than one, a second variable or a function name (sin, sqrt, log), a "/" anywhere (rewrite as a decimal), an unsupported exponent, an equation that expands past degree 2, unbalanced parentheses, or an inequality (<, >). Every refusal includes a fixHint saying exactly what to change. Returns the equation type (linear, quadratic, identity, or contradiction), the standard form, the full list of steps, the solution(s) as exact fractions (with a decimal alongside when not a whole number), and a disclaimer that this covers only one-variable linear and quadratic equations.

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• polymarket_arbitrageA

  LinearA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• resolution_auditA

  LinearA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• ticket_archiveA

  Meta CouncilB

  Retire an owned ticket from the working set, or restore one. Archiving is a different axis from status: the ticket keeps whatever status it had, so a done ticket stays done and a blocked one comes back still blocked — never use status 'cancelled' to mean 'archived'. An archived ticket disappears from ticket_list, the stats, the buckets, the attention queue, and outbound JIRA/Linear sync, but keeps its id and URL so citations stay valid, and it refuses edits until restored. Cascades to the active subtree; restore brings back exactly what was archived alongside it. Nothing is destroyed — this is not a delete, and no delete tool is exposed.

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• get_ilo_detailsA

  Crank ProtocolB

  Read a token launch live from chain (free): terms, status, escrow balances, and a machine-readable liquidity release schedule. ``launch_id`` is the on-chain u64 launch identifier. Returns the raise window and progress, the per-wallet pledge cap (0 = uncapped), live escrow + launch-vault balances, and a ``release_schedule`` block (cliff-then-linear) an agent can plan future claim_ilo_tokens calls from without parsing prose. Pass ``caller_wallet`` to attach that wallet's own pledge summary too (the authoritative per-wallet view is get_my_pledge_status). Utility token fair launch: participating commits funds to a non-custodial escrow; it is not a purchase of an expectation of profit. Workflow: DISCOVER step -- read details, check the window and the per-wallet cap, then preview with pledge_to_ilo (plan mode) before committing anything.

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• analytics_historyA

  CDF FinanceA

  Série histórica contínua (meses com e sem movimento) com saldo reconstruído, patrimônio e estatística elaborada (média/mediana/desvio/variação, tendência por regressão linear, médias móveis 3/6/12m, taxa de poupança). Suporta vida inteira (até 120 meses). Use from/to como YYYY-MM ou YYYY-MM-DD. Use esta ferramenta pro histórico mensal geral (saldo/patrimônio/receita/despesa); para série por categoria use category_history, para separar aporte de valorização em investimentos use wealth_evolution, e para um retrato único do momento atual (não série) use financial_snapshot.

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• equation_steps_runA

  Project GumballA

  Solves a linear or quadratic equation in one variable and returns every step: expanding parentheses, combining like terms, moving terms across the equals sign, and applying the quadratic formula with the discriminant stated. Reports identities and contradictions as such rather than as "no answer", gives roots as exact fractions with a decimal alongside, and labels complex roots explicitly instead of claiming no solution. Refuses rather than guesses on a missing or duplicated "=", a second variable, a function name, an unsupported exponent, or an inequality — every refusal says what to change. Covers only one-variable linear and quadratic equations: not systems, not inequalities, not degree three or higher. WHY DELEGATE THIS: Three places this algebra goes quietly wrong when reasoned about directly: the sign when distributing a negative across parentheses, which direction a term moves as it crosses the equals sign, and dropping one of the two ± roots of a quadratic or rounding a complex pair into a false "no solution". Exact fraction arithmetic gets all three right every time, and showing the work is the point — a student checking their own scratch paper needs the steps, not the answer. Owned by Equation Steps at https://equation-steps.gumballtools.com, which is also callable directly if you would rather not go through the aggregator.

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• export_ticket_to_providerA

  Meta CouncilB

  Export the exact previewed owned ticket to Jira or Linear, using expected_export_hash from preview_ticket_export and the same destination. Requires explicit integrations:write, tickets:write, and independent human approval of the complete preview. Persists the operation before possible remote writes. After an uncertain response, inspect list_ticket_provider_effects and reconcile the original intent; never blindly export again. Remote confirmation is observed, not atomic protection against concurrent remote writers.

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• polymarket_fill_riskA

  LinearA

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.

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• kalshi_weather_edgeA

  LinearA

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

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• pipeworx_feedbackA

  LinearA

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• op_amp_gainA

  calc.engineerA

  Calculate voltage gain and output for operational amplifier circuits in inverting, non-inverting, and differential configurations. For inverting: Av = -Rf/Ri with input impedance equal to Ri. For non-inverting: Av = 1 + Rf/Rg with very high input impedance. For differential: Av = Rf/Ri applied to (V1 - V2). Reports gain in linear and dB, output voltage when input is provided, input impedance, and phase inversion status. Ideal op-amp assumptions (infinite open-loop gain, zero input bias current). Chain from ohms_law for power budgeting or resistor_color_code for component selection.

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• forecast_trendA

  Moltline Data DeskA

  Forecast future periods with a linear trend and honest fit quality. PREMIUM (license). For quick planning, not statistical modeling. Typical input {"values": [100, 120, 138, 161], "periods_ahead": 3} returns {"trend_per_period": 20.2, "r_squared": 0.998, "forecast": [180.9, 201.1, 221.3], "caveat": "..."}. Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• forecast_trendA

  Moltline Data DeskA

  Forecast future periods with a linear trend and honest fit quality. PREMIUM (license). For quick planning, not statistical modeling. Typical input {"values": [100, 120, 138, 161], "periods_ahead": 3} returns {"trend_per_period": 20.2, "r_squared": 0.998, "forecast": [180.9, 201.1, 221.3], "caveat": "..."}. Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• auth_connectA

  RokhaB

  Start an OAuth Connect and get the consent URL to open in a browser. Pass provider (github|google|x|atlassian|microsoft|slack|notion|gitlab|discord|reddit|linear — account-wide, reused everywhere) OR server_url (any MCP server; Rokha runs the MCP auth-spec handshake, grant bound to that server). On approval the token lands in the vault under the returned alias. Requires a logged-in identity.

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• validate_claimA

  LinearA

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

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