"Lua" matching MCP tools:

• asset_lookupA

  RedM McpA

  Resolve a RedM game-data asset (ped model, weapon, object, door, vehicle) by exact name, 32-bit hash, or partial-name search. O(1) structured lookup against pre-parsed discoveries tables — replaces the common workflow of grepping `a_c_bear_01` in peds_list.lua, then cross-referencing RELATIONSHIP/README.md for its relationship group. Returns: type, name, normalized hash (`0x` + 8 uppercase hex), source file + line, plus type-specific metadata (peds get `variants` + `relationship`, weapons get `group`, doors get `coords` + `model_hash`, objects get `category`/`subcategory`). Catalog ~22,500 entries (mostly objects). Typical latency p50 ~15ms, p95 ~65ms. NOT for: - **Script natives** like `SET_ENTITY_COORDS`, `GetPedHealth`, or hashes from `Citizen.InvokeNative(0x...)` — use `lookup_native`. Native hashes are 64-bit (`0x06843DA7060A026B`); asset hashes are 32-bit (`0xBCFD0E7F`). Different namespaces, never collide. - **Flag enums, settings, clipsets, scenario keys** like `CPED_CONFIG_FLAGS`, `MP_Style_Casual`, `mech_loco_m@`, `MAGGIE_SEAT_CHAIR_DESK_WRITING`. Those live as tokens in lua source but not in this catalog. Use `grep_docs`. - **Behavior queries** ("which animal is the bear", "weapons in the lemat family") — use `semantic_search`. Pass exactly ONE of `name` / `hash` / `search`. Optional `type` narrows to a category (useful when a fragment like "horse" hits both peds and vehicles). Note: `type` reflects the SOURCE FILE — the same asset name can exist under multiple `type`s. e.g. `mp006_p_mshine_int_door01x` appears as `type=object` (1 row from object_list.lua) AND `type=door` (2 rows from doorhashes.lua, different door hashes for distinct in-world instances with `coords`). Pick `type=door` when you want lockable in-world doors with positions; `type=object` for the model itself. Examples: - `{name: "a_c_bear_01"}` → exact ped lookup, returns variants=11 + relationship=REL_WILD_ANIMAL_PREDATOR. - `{hash: "0xBCFD0E7F"}` → resolves to ped `a_c_bear_01` (omit `0x` ok). - `{search: "lemat", type: "weapon"}` → substring match → `weapon_revolver_lemat`. - `{search: "moonshine", type: "door"}` → exact substring misses (no door name contains "moonshine"), fuzzy trigram fallback fires → `mp006_p_mshine_int_door01x`. Fuzzy mainly fires when `type` narrows out the exact-substring matches; without `type`, common terms find substring hits first and never reach fuzzy.

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• asset_lookupA

  redm-mcpA

  Resolve a RedM game-data asset (ped model, weapon, object, door, vehicle) by exact name, 32-bit hash, or partial-name search. O(1) structured lookup against pre-parsed discoveries tables — replaces the common workflow of grepping `a_c_bear_01` in peds_list.lua, then cross-referencing RELATIONSHIP/README.md for its relationship group. Returns: type, name, normalized hash (`0x` + 8 uppercase hex), source file + line, plus type-specific metadata (peds get `variants` + `relationship`, weapons get `group`, doors get `coords` + `model_hash`, objects get `category`/`subcategory`). Catalog ~22,500 entries (mostly objects). Typical latency p50 ~15ms, p95 ~65ms. NOT for: - **Script natives** like `SET_ENTITY_COORDS`, `GetPedHealth`, or hashes from `Citizen.InvokeNative(0x...)` — use `lookup_native`. Native hashes are 64-bit (`0x06843DA7060A026B`); asset hashes are 32-bit (`0xBCFD0E7F`). Different namespaces, never collide. - **Flag enums, settings, clipsets, scenario keys** like `CPED_CONFIG_FLAGS`, `MP_Style_Casual`, `mech_loco_m@`, `MAGGIE_SEAT_CHAIR_DESK_WRITING`. Those live as tokens in lua source but not in this catalog. Use `grep_docs`. - **Behavior queries** ("which animal is the bear", "weapons in the lemat family") — use `semantic_search`. Pass exactly ONE of `name` / `hash` / `search`. Optional `type` narrows to a category (useful when a fragment like "horse" hits both peds and vehicles). Note: `type` reflects the SOURCE FILE — the same asset name can exist under multiple `type`s. e.g. `mp006_p_mshine_int_door01x` appears as `type=object` (1 row from object_list.lua) AND `type=door` (2 rows from doorhashes.lua, different door hashes for distinct in-world instances with `coords`). Pick `type=door` when you want lockable in-world doors with positions; `type=object` for the model itself. Examples: - `{name: "a_c_bear_01"}` → exact ped lookup, returns variants=11 + relationship=REL_WILD_ANIMAL_PREDATOR. - `{hash: "0xBCFD0E7F"}` → resolves to ped `a_c_bear_01` (omit `0x` ok). - `{search: "lemat", type: "weapon"}` → substring match → `weapon_revolver_lemat`. - `{search: "moonshine", type: "door"}` → exact substring misses (no door name contains "moonshine"), fuzzy trigram fallback fires → `mp006_p_mshine_int_door01x`. Fuzzy mainly fires when `type` narrows out the exact-substring matches; without `type`, common terms find substring hits first and never reach fuzzy.

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• polymarket_arbitrageA

  Statec LuA

  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

  Statec LuA

  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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• polymarket_fill_riskA

  Statec LuA

  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

  Statec LuA

  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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• release_calendar_marketsA

  Statec LuA

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns error:"no_releases_in_window" with a widen-the-window hint rather than an empty array — econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• pipeworx_feedbackA

  Statec LuA

  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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• list_dataflowsA

  Statec LuA

  Browse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an `id` (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass `query` to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).

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• get_dataA

  Statec LuA

  Pull observations from a STATEC dataset. `key` is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Fetch dataflow_structure first to know the dimension order and valid codes. Example: get_data({ dataflow_id: "DF_A1100", key: "Valeur..A", start_period: "2010", end_period: "2020" }) picks VARIABLE=Valeur, wildcards SPECIFICATION, FREQ=A (annual). Omit `key` (or pass "") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values.

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• lookup_nativeA

  RedM McpA

  Resolve a RedM/RDR3 SCRIPT native by hash or name — O(1), exact. Use whenever you see `Citizen.InvokeNative(0x...)`, `Citizen.invokeNative('0x...')`, `GetHashKey('NAME')`, or a SCREAMING_SNAKE_CASE native name (e.g. `SET_ENTITY_COORDS`, `GetPedHealth`) in Lua/JS/TS. NOT for game-data hashes (weapon/ped/animation names) — use `grep_docs`. Pass `hash` (0x… optional, case-insensitive) or `name` (exact first, ILIKE substring fallback). Returns name, hash, namespace, return type, params, description, full content, plus `findings[]` — community gotchas linked to that native. Inspect `findings[].id` and call `get_document({path: 'learning:<id>'})` for full body. Also returns `refDocs[]` — enum/flag value tables for that native (the constants to pass for params like flagId/attributeIndex/eventType). When `refDocs[].content` is set, it's the inline enum table — use those values directly. When `content` is null but `refDocs[].fetch` is present, the table was too large to inline — run that exact call (e.g. `get_document({ path: "refdoc:eEventType" })`) to get the full table; `refDocs[].preview` shows the first lines. github entries (no `fetch`) are url-only. A miss also reports if the native exists in the other game (GTA5/FiveM) — do not call those in RedM — and says so when it is in neither database (likely fabricated).

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• lookup_nativeA

  redm-mcpA

  Resolve a RedM/RDR3 SCRIPT native by hash or name — O(1), exact. Use whenever you see `Citizen.InvokeNative(0x...)`, `Citizen.invokeNative('0x...')`, `GetHashKey('NAME')`, or a SCREAMING_SNAKE_CASE native name (e.g. `SET_ENTITY_COORDS`, `GetPedHealth`) in Lua/JS/TS. NOT for game-data hashes (weapon/ped/animation names) — use `grep_docs`. Pass `hash` (0x… optional, case-insensitive) or `name` (exact first, ILIKE substring fallback). Returns name, hash, namespace, return type, params, description, full content, plus `findings[]` — community gotchas linked to that native. Inspect `findings[].id` and call `get_document({path: 'learning:<id>'})` for full body. Also returns `refDocs[]` — enum/flag value tables for that native (the constants to pass for params like flagId/attributeIndex/eventType). When `refDocs[].content` is set, it's the inline enum table — use those values directly. When `content` is null but `refDocs[].fetch` is present, the table was too large to inline — run that exact call (e.g. `get_document({ path: "refdoc:eEventType" })`) to get the full table; `refDocs[].preview` shows the first lines. github entries (no `fetch`) are url-only. A miss also reports if the native exists in the other game (GTA5/FiveM) — do not call those in RedM — and says so when it is in neither database (likely fabricated).

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• deep_researchA

  Statec LuA

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1659 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 6,350 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• validate_claimA

  Statec LuA

  "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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• grep_docsA

  RedM McpA

  Find exact tokens or patterns in raw documentation, including large Lua tables and community-native notes. Use to find every source reference, walkstyle/clipset names such as MP_Style_Casual, scenario keys, framework API symbols, propsets, vehicle bones, drawable/material data or entity extensions. For complete animation/audio/IMAP/clothing/wearable/particle/flag/texture records prefer discovery_lookup; for ped/weapon/object/door/vehicle models use asset_lookup; for callable native hashes/names use lookup_native; for behavior explanations use semantic_search. grep_docs remains the fallback for unsupported formats or a catalog miss. Optional contextBefore/contextAfter includes surrounding lines; filesOnly returns paths; multiline permits cross-line patterns. Patterns use Rust regex syntax. Prefer targeted patterns over large alternations. Returns path and 1-based line numbers; long lines are windowed around the match. Read surrounding raw content with read_lines({path,start}), including content beyond a document preview. If retrying a failed pattern, populate prior_attempt.

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• grep_docsA

  redm-mcpA

  Find exact tokens or patterns in raw documentation, including large Lua tables and community-native notes. Use to find every source reference, walkstyle/clipset names such as MP_Style_Casual, scenario keys, framework API symbols, propsets, vehicle bones, drawable/material data or entity extensions. For complete animation/audio/IMAP/clothing/wearable/particle/flag/texture records prefer discovery_lookup; for ped/weapon/object/door/vehicle models use asset_lookup; for callable native hashes/names use lookup_native; for behavior explanations use semantic_search. grep_docs remains the fallback for unsupported formats or a catalog miss. Optional contextBefore/contextAfter includes surrounding lines; filesOnly returns paths; multiline permits cross-line patterns. Patterns use Rust regex syntax. Prefer targeted patterns over large alternations. Returns path and 1-based line numbers; long lines are windowed around the match. Read surrounding raw content with read_lines({path,start}), including content beyond a document preview. If retrying a failed pattern, populate prior_attempt.

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• demarche_urbanisme_projetA

  Pillr, la donnée immobilière françaiseA

  Quelle formalité d'urbanisme pour un projet de travaux (aucune, déclaration préalable, permis de construire, d'aménager ou de démolir), quel formulaire Cerfa, un architecte est-il obligatoire, quelles pièces, quel délai d'instruction. Chaque conclusion porte sa règle et sa source, et la base de règles porte sa date. Un fait manquant revient en question, jamais en supposition. Sans adresse : ce connecteur ne lit ni le PLU ni les servitudes, les protections du terrain sont à déclarer en paramètres. Information générale, ni conseil juridique ni décision de la mairie.

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• discovery_lookupA

  RedM McpA

  Look up complete structured RedM discoveries records: animation dictionary + clip, audio bank/soundset + sound, IMAP hash + coordinates, clothing items and wearable states, particle dictionary + effect, named flags, and textures with image links. Use for literal identifiers even deep inside the largest Lua tables. Pass name for exact member lookup, parent for exact dictionary/bank/group, search for keywords or a partial identifier, or hash for a 32-bit hex/decimal hash. Optional kind restricts domain; near finds IMAPs in a 3D radius. Returns complete records with source commit and line range; path discovery:<id> can be opened with get_document. Paginate using returned nextOffset. For ped/weapon/object/door/vehicle model lookup use asset_lookup; for callable 64-bit natives use lookup_native; for behavior explanations use semantic_search. Examples: {kind:'animation',name:'ig2_jobbriefs_handover_p_woodstake01x'}, {kind:'animation',search:'piano'}, {kind:'imap',name:'bone_05'}, {kind:'wearable',name:'CLOTHING_ITEM_F_BOOTS_017_TINT_002'}. RedM only.

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• discovery_lookupA

  redm-mcpA

  Look up complete structured RedM discoveries records: animation dictionary + clip, audio bank/soundset + sound, IMAP hash + coordinates, clothing items and wearable states, particle dictionary + effect, named flags, and textures with image links. Use for literal identifiers even deep inside the largest Lua tables. Pass name for exact member lookup, parent for exact dictionary/bank/group, search for keywords or a partial identifier, or hash for a 32-bit hex/decimal hash. Optional kind restricts domain; near finds IMAPs in a 3D radius. Returns complete records with source commit and line range; path discovery:<id> can be opened with get_document. Paginate using returned nextOffset. For ped/weapon/object/door/vehicle model lookup use asset_lookup; for callable 64-bit natives use lookup_native; for behavior explanations use semantic_search. Examples: {kind:'animation',name:'ig2_jobbriefs_handover_p_woodstake01x'}, {kind:'animation',search:'piano'}, {kind:'imap',name:'bone_05'}, {kind:'wearable',name:'CLOTHING_ITEM_F_BOOTS_017_TINT_002'}. RedM only.

  ConnectorOAuth

• ask_pipeworx_groundedA

  Statec LuA

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 6,350 across 1659 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

  ConnectorNo auth

• entity_profileA

  Statec LuA

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each entry carrying a `reason` token and a plain-English `detail` (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

  ConnectorNo auth