"Kit" matching MCP tools:

• recent_changes

  Dati It

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• analyze_business_vertical

  agent-ready-kit

  Use this immediately after scan_site to give the user a 'what this means for my business' framing. Detects the site's business vertical (auto dealership, law firm, healthcare, home services, ecommerce, digital agency, etc.) from JSON-LD schema + scraped text. Returns expected AI-search lift %, current competitor adoption %, and a positioning pitch tailored to the vertical. **If `should_ask_user` is true, the detection is low-confidence — ASK THE USER what category their business is in before continuing, rather than acting on the guessed vertical.** Also returns the site title and meta description so the calling agent can render a Site Summary card.

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• entity_profile

  Istat It

  "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, news, GLEIF and returns: cik + company_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); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

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• recent_changes

  Istat It

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• polymarket_arbitrage

  Dati It

  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}. 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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• remember

  Dati It

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• deep_research

  Dati It

  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 1340 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 5,093 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 (record-level pipeworx:// when the source emits one, else source-level). "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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• deep_research

  Istat It

  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 1340 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 5,093 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 (record-level pipeworx:// when the source emits one, else source-level). "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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• polymarket_fill_risk

  Dati It

  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).

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• polymarket_fill_risk

  Istat It

  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).

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• niche_brand_kit_ingest

  Niche — Editorial Intelligence

  Auto-populate the user's BrandKit (palette / fonts / tagline / logo / wordmark / boilerplate / voice notes) from files, a URL, or pasted text. Additive by default: fills empty fields, leaves populated ones alone. Idempotent: re-running the same inputs doesn't double-write. Overwrite rule: if the target brand kit already has an identity (a tagline/boilerplate/voice for a different brand), do not silently overwrite it. First ask the user whether to replace it. If the account supports multiple brand profiles, prefer creating a separate brand instead: pass a new `brand_id` slug plus `brand_name` rather than clobbering the existing one. Only pass `replace=true` once the user has confirmed they want this brand re-learned from the new source. Use when the agent has brand assets in scope (a working directory with logos / press-kit / brand-guide PDFs, the user's portfolio or Substack URL, pasted boilerplate copy) and wants to populate Niche's BrandKit so future signal_scan and content generation inherit the brand context. Agent-side equivalent of the Niche web app brand-kit ingest surface, same backend engine. Async, then poll: a URL or multi-file ingest runs in the background, so this call returns fast with {ingest_id, status:'ingesting'}. Then poll niche_brand_kit_ingest_status(ingest_id) until status is 'done'; that response carries the populated BrandKit, the ingest report (detected[] / skipped[] / errors[]), and a diff[] of changed fields. (Loop: ingest, then poll status until done/failed; same pattern as niche_signal_scan to niche_session_state.) Do not re-call ingest while one is running; a duplicate of the same inputs attaches to the in-flight job. URL ingest also fills voice primitives when the page has post-shaped text (Substack/blog/X). If a URL is slow or thin to scrape, the visual fields may land before the voice pass completes; when the report flags this, paste the page's About/homepage copy via `text=` to complete the brand voice.

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• verify_deployment

  agent-ready-kit

  Use this after a customer has deployed the agent-ready files to confirm the live site reaches the expected level. Re-scans the site, reports current level, which checks pass, and which still fail with their fix string. Same scanner as scan_site, but framed as post-deploy verification rather than initial assessment.

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• request_invoice

  agent-ready-kit

  Use this to autonomously close a deal: issue a ONE-TIME Stripe Invoice for the value-based amount the buyer agreed to (one-and-done — no recurring). **Amount source (required — one of):** pass `quote_id` (preferred — bills the agreed figure from a quote the buyer was shown) OR an explicit buyer-confirmed `amount_usd`. Never invent an amount without one of these. **Minimum $350** for any paid engagement (the free scan + starter files stay free). **Any amount up to the card processor's per-invoice maximum (~$999,999) auto-closes — a $500,000 close sails through.** Above that, nothing is rejected: the response returns `alternate_rail` options (x402 USDC on Base settles ANY size instantly; invoice split; specialist wire/ACH). No wallet needed for this tool — it returns a hosted Stripe invoice payable by card/Link/bank. Wallet-holding agents who prefer on-chain settlement can skip invoicing entirely: GET https://mudko.com/api/x402/purchase (x402 v2, USDC on Base). **Only `email` (and `domain`) are required.** Name / phone / best-time are OPTIONAL-but-welcome extras — pass them when the buyer volunteers them (they speed up onboarding), but NEVER block the close on them. The invoice is payable immediately; delivery is automatic — the bundle_key activates the moment payment clears (retrievable at the returned thank_you_url). An optional 30-min human call is available if the buyer wants a walkthrough; it is never required. Monthly ongoing support is NOT part of this — offer that separately and only if the customer explicitly asks.

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• ask_pipeworx

  Dati It

  PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,093 tools across 1340 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

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• scan_dependency

  Dati It

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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• niche_brand_kit_update

  Niche — Editorial Intelligence

  Set specific BrandKit fields by name. The write path for the structured fields (tagline / boilerplate / voice_notes / forbidden_phrases / signature_phrases / endcard preferences / video voice preference / colors / fonts) without going through the ingest engine. Use after niche_brand_kit_ingest fills the easy stuff, or to commit values the user answered through niche_brand_kit_guided_setup. Only fields you pass are touched; fields you omit stay at their current value. Lists replace the current value (they do not append). Response includes a diff[] of fields that changed and the full updated kit. Archiving: pass archive=true to archive a brand (soft and reversible; it disappears from every list but is not deleted), or archive=false to restore one. The default/active brand can't be archived (promote another to default first). A brand with published history won't archive unless you also pass acknowledge=true. Use archive_scope='test' to archive every scratch brand at once (never the default). There is no hard delete here; archive is the removal verb agents have.

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• post_lint_elixir

  webbersites-x402-data-api

  Elixir lint / debug kit: POST {code}, get the bugs back with line numbers. Deterministic static analysis — code is parsed, never executed, no AI. Catches unbalanced do/end and brackets, missing do, 'return', '+' string concat, trailing commas, field assignment, = vs == in conditions and guards, block-scoped rebinding (the classic), charlist vs String mixups, unused variables. Errors, warnings and hints with fixes. Max 128 KB. ($0.002 per call, paid via x402)

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• request_datasheet_upload

  sheetsdata-mcp

  Request a signed URL to upload a datasheet PDF for a component whose datasheet we don't have. Use this when search_parts / get_part_details / prefetch_datasheets return datasheet_status='no_source' (and a retry didn't help) or 'unsupported'. Free — the upload fee is only charged on confirm_datasheet_upload after we validate the file. Flow (3 steps): 1. Call request_datasheet_upload with the MPN, the file's SHA-256, and its byte size. You get back an upload_url, upload_method ('PUT'), upload_headers, and an opaque upload_token. 2. Upload the PDF directly to the returned URL with curl: `curl -X PUT -H 'Content-Type: application/pdf' --data-binary @file.pdf "$UPLOAD_URL"` (add any headers from upload_headers). 3. Call confirm_datasheet_upload with the upload_token. Server verifies the bytes, re-hashes, checks for the MPN on the first page, charges the upload fee (50¢), and queues extraction. Returns document_id + status='pending'. Validation rules (checked at confirm time, refunded on failure): - File must be a valid PDF (magic bytes + parseable). - Actual SHA-256 must match expected_sha256. - Actual byte size must match size_bytes (±0). - MPN or its core stem must appear in the first page text (catches wrong-file uploads). Scanned image-only PDFs will fail this check — upload a text-based PDF. - Max 50MB per file. No dev-kit manuals / BOB schematics / app-notes as datasheets — use the matching MPN's actual datasheet. Uploaded datasheets are scoped to your organization (private). They satisfy read_datasheet, search_datasheets, check_design_fit, and analyze_image for your org's tokens only. Tokens expire after 15 minutes. If upload fails or times out, just call request_datasheet_upload again.

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• update_html

  Dock

  Write an HTML surface's body. Pass any of `html` / `css` / `js`; omitted fields stay unchanged. Pass empty string to clear. The surface renders in a sandboxed iframe on a separate origin (`render.trydock.ai`) with no access to Dock cookies, storage, or parent DOM — you have free rein inside that boundary. Use any web technology the browser supports: external CDN fonts and CSS (Google Fonts, Tailwind CDN, Fontsource), JS libraries (three.js, GSAP, Chart.js, anime.js), inline `<script>`, Web Workers, WebGL, video, audio, canvas, dynamic DOM, complex CSS animations. Per-field caps: html 256 KB, css 200 KB, js 200 KB, total 600 KB. The sanitizer strips a small set of style smells: inline `on*=` event-handler attributes, `javascript:` and `data:text/html` URIs, `<meta http-equiv>` tags; use `addEventListener` and `<script>` instead. Layout: Dock renders the surface EDGE-TO-EDGE (full-bleed) inside the workspace — the surface itself is the frame. Do NOT put `border-radius`, an outer border, or a drop-shadow on the root/outermost element unless the owner explicitly asked for that framing, or the specific design genuinely needs it; keep the page root flush and apply rounding to inner cards only. DESIGN LANGUAGE: Dock injects a base stylesheet into every surface — semantic tokens + a small component kit — that automatically follows each VIEWER's light/dark theme. PREFER these over hardcoded colors so the surface matches Dock and themes correctly for everyone (a surface with hardcoded dark colors looks broken for a light-mode teammate on a shared surface, and vice-versa). Tokens: var(--dock-canvas|surface|surface-muted|border|border-strong|text|text-2|text-muted|accent|accent-ink|data|data-strong|good|warn|crit), var(--dock-radius|shadow|gap); font is Inter via var(--dock-font). Component classes: .dock-card, .dock-stat/.dock-stat-value/.dock-stat-label, .dock-delta.up|.down, .dock-badge.good|warn|crit|neutral|accent (add a <span class="dot"></span>), .dock-btn(.primary), .dock-table (use td.name for the primary cell, .dock-num for tabular figures), .dock-grid, .dock-eyebrow, .dock-row, .dock-avatar, .dock-field + .dock-input, .dock-bars/.dock-bar(.hot). Put .dock-num on any number so it aligns. This is only a DEFAULT floor — write your own CSS to override any of it; nothing in the baseline is !important, so a surface that brings its own styles always wins. Requires editor role.

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• discover_tools

  Dati It

  Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

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