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650,501 tools. Updated 2026-10-13 22:52

"Spring" matching MCP tools:

  • Generate a color harmony from a base color. base accepts a hex, CSS name, RNV brand name, or saved-palette reference (e.g. 'Spring line:2'). scheme is one of: complementary, analogous, triadic, split-complementary, tetradic (a.k.a. square), monochromatic, compound. Returns a list of hex colors. Read-only and deterministic: it derives the colors from the base and stores nothing, so it has no side effects and is safe to call repeatedly. Use to expand one base color into a related set; to blend existing colors into a single color use mix_colors, and to persist a set you like use save_palette.
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  • Generate a color harmony from a base color. base accepts a hex, CSS name, RNV brand name, or saved-palette reference (e.g. 'Spring line:2'). scheme is one of: complementary, analogous, triadic, split-complementary, tetradic (a.k.a. square), monochromatic, compound. Returns a list of hex colors. Read-only and deterministic: it derives the colors from the base and stores nothing, so it has no side effects and is safe to call repeatedly. Use to expand one base color into a related set; to blend existing colors into a single color use mix_colors, and to persist a set you like use save_palette.
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  • Submits an approval request of any supported type: UAT_PROMOTION, UAT_SIGN_OFF, PRODUCTION_PROMOTION, VISIBILITY_CHANGE, OWNERSHIP_TRANSFER, RETIREMENT, DOMAIN_CHANGE, or ROLLBACK. **An agent may submit but never decide**: SpringRoll requires a human approver, and an agent token cannot approve its own request. Confirm with the user before calling this. It creates review work for named people. Type-specific behaviour: • `PRODUCTION_PROMOTION`: this does NOT deploy. It asks. `releaseId` defaults to the application's latest release, and `targetEnvironmentType` to 'production'. Once approved, call springroll.deploy.promote. • `UAT_PROMOTION`: `targetEnvironmentType` is required, 'uat' (or 'development' where Development needs approval). An approval only unlocks the environment it names. • `UAT_SIGN_OFF`: `targetEnvironmentType` defaults to 'uat'. • `ROLLBACK`: `justification` and `targetEnvironmentType` (the environment to roll back) are required. Put the deployment to restore in `payload.targetDeploymentId`; it defaults to the last successful one. • `RETIREMENT`: `justification` is required. • Every other type takes no `targetEnvironmentType`.
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  • Convert a wall-clock time between two IANA timezones, and report the ways the conversion can be wrong. ALWAYS use this instead of doing the arithmetic. Timezone conversion looks like addition and is not: the mapping from a local time to an instant is not a function. - On the spring-forward date, an hour of local time DOES NOT EXIST. Asked to convert 02:30 on that date, a model will return a plausible timestamp for a time that never happens. This tool returns null and says why. - On the fall-back date, an hour happens TWICE. There are two correct answers an hour apart; this returns both rather than silently choosing. - The gap between two zones is not constant. Zones start and end daylight saving on different dates, so a cached offset is wrong for weeks each year. - Not all offsets are whole hours: India is +05:30, Nepal +05:45. Input: `time` must be YYYY-MM-DD HH:MM (or with T, and optional seconds) — free-form dates are refused rather than guessed. `from` and `to` must be full IANA names such as "America/New_York". ABBREVIATIONS ARE REJECTED, deliberately, even though most runtimes accept them. Node resolves "BST" to Bangladesh Standard Time (UTC+06:00) when nearly everyone writing it means British Summer Time (UTC+01:00) — a five-hour error that yields a perfectly plausible timestamp. Same for CST, IST, PST, and EST. Returns: the chosen UTC instant (null if the local time does not exist), every candidate instant, both zone readings with the offset and abbreviation that applied on THAT date, the difference between the zones, upcoming clock changes for both, and warnings. Check `warnings` for severity "error" before using the result.
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  • 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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  • 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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  • Query verified ERCOT wholesale electricity prices — ERCOT's Day-Ahead Market Settlement Point Prices ($/MWh, EMIL NP4-190-CD), the price cleared the day before each operating day at every ERCOT (Texas) settlement point, served hourly. Returns cited prices for each (`settlement_point`, `delivery_date`, `hour_ending`): the per-hour `price_usd_per_mwh` in detail records, plus `avg_price_usd_per_mwh`, `min_price_usd_per_mwh`, and `max_price_usd_per_mwh` over the result scope. Each `settlement_point` is one of ERCOT's locations — a trading Hub (e.g. `HB_NORTH`, `HB_HOUSTON` — the regional benchmark prices), a Load Zone (e.g. `LZ_HOUSTON`), a Resource Node (one generator's connection point), or a DC-tie — and `settlement_point_type` carries ERCOT's OWN verbatim classification code (`HU`/`LZ`/`RN`/`LZ_DC` and finer codes) so an agent can tell a regional benchmark from a single-plant node. Filter or group by `settlement_point`, `settlement_point_type`, `hour_ending`, or `delivery_date`; filter a date window with `delivery_date_from` / `delivery_date_to`, or one day with `delivery_date`. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"settlement_point": "HB_NORTH", "delivery_date": "2026-06-20", "group_by": ["hour_ending"]}` for the North hub's 24 hourly day-ahead prices; `{"delivery_date": "2026-06-20", "group_by": ["settlement_point_type"]}` for the average price by location type. With no date filter the result defaults to the latest delivery day with prices (it does not scan all history); served delivery coverage begins 2014-05-02 from ERCOT's official Data Access Portal archive. Historical settlement-point names remain exactly as published, and names absent from the current pinned type list have a null type rather than being rewritten. Returns JSON with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of` (the delivery day), and a `source_row` verifiable with get_source_evidence_v1. A price is INTENSIVE ($/MWh): it is AVERAGED, min'd, and max'd over a scope — NEVER summed (a "total price" is meaningless, so no sum is offered). An average across more than one settlement point (e.g. a hub and a resource node together) is indicative, not a settlement value — group_by `settlement_point` for the per-point series, or filter to one point/type. This is the DAY-AHEAD hourly market, NOT real-time / 5-minute prices. ERCOT's day-ahead settlement-point price is a TOTAL only — there is no energy/congestion/loss component split and no loss component, and none is synthesized. ERCOT's own hour-ending label (`01:00`..`24:00`) and `dst_flag` are carried verbatim (a day is 24 hours normally, 25 on the fall-back DST date with `02:00` repeated, 23 on spring-forward). A settlement point is an electrical/aggregate location, not a plant — ERCOT supplies no county or lat/lon, so the only geography anchor is `state` = TX. This is a PRICE ($/MWh) — not capacity (MW) or generation (MWh): for installed/operating capacity use query_power_capacity_v1, for electricity generated use query_power_generation_v1. ERCOT only — prices are NEVER blended, averaged, or compared across ISOs (each ISO's market design and redistribution license differ); this tool serves ERCOT's day-ahead settlement-point prices alone.
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  • Replace the entire project with the given source JSON, returning its project_id. This is the PRIMARY way to build: use it to create a composition or to add/change many elements at once. (To tweak a single element in an existing project, use edit_element / add_element / delete_element instead.) REVISION RULE: if you are iterating on a video that already exists in this conversation — the user asked for changes, you are fixing your own work — you MUST pass its project_id so the project is updated in place and keeps one editor link and its version history. Omitting project_id creates a NEW, unrelated project and strands the old one; only omit for a genuinely different video. The input is validated against the @clipkit/protocol before being accepted; invalid inputs return an error. Shape: { width, height, duration, frame_rate, output_format, background_color?, defaults? (house easing, e.g. a spring), fonts?, camera?, lights?, elements:[…] }; every element has a `type` plus base fields (id, layer, x, y, width, height, time, duration, opacity, rotation, animations, keyframe_animations) and type-specific fields. CKP/1.1: keyframe easings accept a spring object {type:"spring", duration?, bounce?}; channels take stagger {each, from, ease, split, seed} to spread across text units / set copies / group children; groups take layout {direction, gap, align} (stack — no hand-computed positions); text takes a count-up {expr, format}. For exact field names + types call get_schema (optionally with an element_type) — the runtime ignores unrecognized keys, and this tool flags any it does not recognize.
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  • Build a UTM-tagged URL for campaign tracking. FREE. Typical input {"url": "https://example.com/pricing", "source": "newsletter", "medium": "email", "campaign": "spring-launch"} returns {"tagged_url": "https://example.com/pricing?utm_source=newsletter& utm_medium=email&utm_campaign=spring-launch"}. Use to build one tagged tracking URL. Not for analyzing campaign results and not for anything that belongs in the message body (audit_copy). 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": "url must start with http(s)://"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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  • Build a UTM-tagged URL for campaign tracking. FREE. Typical input {"url": "https://example.com/pricing", "source": "newsletter", "medium": "email", "campaign": "spring-launch"} returns {"tagged_url": "https://example.com/pricing?utm_source=newsletter& utm_medium=email&utm_campaign=spring-launch"}. Use to build one tagged tracking URL. Not for analyzing campaign results and not for anything that belongs in the message body (audit_copy). 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": "url must start with http(s)://"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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  • Registers the project if it is new, attaches whatever source you give it, and deploys it using the application's Deployment workflow. Direct applications go to Production; Staged applications go to Development. Returns the live URL, or a deployment id to poll if the build is still running. **You do not decide whether this project needs git.** Send what you have and SpringRoll works it out: • a pushed git remote → pass `repositoryUrl` (and `ref` if not the default branch) • no remote, or uncommitted work → pass `archive`, a base64 tar+gzip of the source: tar --exclude=node_modules --exclude=.next --exclude=.git --exclude='.env*' \ -czf - . | base64 -w0 • both → SpringRoll builds from git, and falls back to your files if the ref cannot be resolved (an unpushed branch, typically) • neither, on an app that already exists → redeploys its current source Upload SOURCE, not build output: SpringRoll runs the build. node_modules, .next, dist, build, out, coverage and .log files are dropped automatically and reported. Every .env file and .git/ is REFUSED outright, naming the offending path: configuration belongs in SpringRoll, not in the bundle. Upload limits are about 3 MB compressed on the wire (a platform request-body cap, not a preference), 20 MB expanded, 2000 files, 512 KB per file; a project past them should pass `repositoryUrl` instead, which SpringRoll clones directly with no size limit. Sending the same files twice is free, because bundles are addressed by content. Direct to Production skips workflow approvals but keeps production safety checks. Staged applications continue to use explicit promotion and approvals.
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  • Attaches the conversation that produced this application to its record, as reference for whoever maintains it next and for the reviewer who has to approve it. Ask the user before calling this, every time. The transcript is STORED and is READABLE BY ANYONE who can see the application record. SpringRoll scrubs credentials it recognises before writing (agent tokens, cloud keys, private keys, connection strings, and assignment lines whose key looks like a secret), but that scrubbing is best-effort and cannot be complete. If the conversation contained a real secret, do not record it. Send turns in order. Keep `sessionKey` stable across calls for one build so they join up; retrying with the same key and turn indexes overwrites rather than duplicating.
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  • 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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  • 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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  • 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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  • Persist a named color palette for later retrieval with get_palette or list_palettes. Each entry in colors accepts what every other tool accepts: a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold), or a saved-palette reference ('Spring line:2'); each is resolved and stored as normalized hex, and an unknown token refuses the whole save naming its position. Optional notes are stored as the palette's description. Author is recorded as RNVizion. The name is refused, with the reason, if it is an RNV brand name, a CSS color name, a 'css:' form, a hex literal, or contains ':' -- a palette resolves ahead of brand and CSS names, so such a name would redefine a color for every caller. This WRITES to the palette store and is the only tool here that does. Reusing an existing name overwrites that palette: save and update are the same call (an upsert), there is no separate update operation. Returns a `durable` flag: true if the palette reached durable storage (the HF Dataset) and will survive a restart, false if it saved to the local working copy only (which is lost on rebuild), with `durable_reason` naming the step that stopped it. Use when the user wants to keep a set of colors under a name for reuse across sessions, such as a brand or launch palette; to read a palette back use get_palette, and to see what already exists use list_palettes. The saved name can then be passed to mix_colors, convert_color, and generate_harmony as a palette reference.
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  • Find USGS water monitoring sites in a state. Returns a list of monitoring stations with their site numbers, names, and locations. Use site numbers with get_water_levels to retrieve data. Args: state: Two-letter US state abbreviation (e.g. 'CA', 'TX'). site_type: Type of monitoring site. 'ST' for stream/river, 'GW' for groundwater well, 'SP' for spring. limit: Maximum number of sites to return (default 50).
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  • Persist a named color palette for later retrieval with get_palette or list_palettes. Each entry in colors accepts what every other tool accepts: a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold), or a saved-palette reference ('Spring line:2'); each is resolved and stored as normalized hex, and an unknown token refuses the whole save naming its position. Optional notes are stored as the palette's description. Author is recorded as RNVizion. The name is refused, with the reason, if it is an RNV brand name, a CSS color name, a 'css:' form, a hex literal, or contains ':' -- a palette resolves ahead of brand and CSS names, so such a name would redefine a color for every caller. This WRITES to the palette store and is the only tool here that does. Reusing an existing name overwrites that palette: save and update are the same call (an upsert), there is no separate update operation. Returns a `durable` flag: true if the palette reached durable storage (the HF Dataset) and will survive a restart, false if it saved to the local working copy only (which is lost on rebuild), with `durable_reason` naming the step that stopped it. Use when the user wants to keep a set of colors under a name for reuse across sessions, such as a brand or launch palette; to read a palette back use get_palette, and to see what already exists use list_palettes. The saved name can then be passed to mix_colors, convert_color, and generate_harmony as a palette reference.
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  • "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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  • Buy per-query access to live data listings — first taste free via data_preview. Requires an agent API key (Authorization: Bearer or X-API-Key). Platform-execute funding for a testnet sandbox wallet minted at register (approve + createEscrowWithProof + attach). Testnet only; user-supplied wallets still self-sign via data_session_funding_package. Not guest-callable. No REST analogue.
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