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276,925 tools. Last updated 2026-07-09 06:34

"namespace:io.github.Latimer-Woods-Tech" matching MCP tools:

  • Returns a paginated list of domains from the tracker database. Results are ordered alphabetically by domain name and support cursor-based pagination for full traversal. Filtering by category and minimum score allows targeted data extraction. Use this tool when: - You want to enumerate all known ad-tech or analytics domains above a risk threshold. - You need a dataset of tracker domains for offline analysis. - You are paginating through a category to build a block list. Do NOT use this tool when: - You need data for a specific domain — use `get_domain` instead. - You are searching by keyword — use `search` instead. - You want domains belonging to a specific company — use `get_entity` instead. Inputs: - `category` (query, optional): Filter by surveillance category. One of: `ad_tech`, `analytics`, `social`, `fingerprinting`, `content`, `cdn`, `other`. - `min_score` (query, optional): Integer 0-100. Exclude domains scoring below this value. - `limit` (query, optional): Number of results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from the previous response's `next_cursor` field. Returns: - Array of domain list items (domain, category, score, prevalence, entity summary). - `meta.has_more`: true if more pages exist. - `meta.next_cursor`: pass as `cursor` to get the next page. - `meta.count`: number of results in this page. Cost: - Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page. Latency: - Typical: <200ms, p99: <500ms.
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  • Returns a paginated list of corporate entities in the TunnelMind surveillance database. Includes data categories, estimated data value, and industry classification. Useful for enumerating the surveillance ecosystem by sector. Use this tool when: - You want to enumerate all entities in a specific industry (e.g., all ad-tech companies). - You need a dataset of surveillance entities for analysis or reporting. - You are building a comprehensive surveillance landscape map. Do NOT use this tool when: - You need the full profile of a specific entity — use `get_entity` instead. - You are searching by entity name — use `search` instead. - You need domain-level data — use `list_domains` instead. Inputs: - `industry` (query, optional): Filter by industry classification. Examples: `ad_tech`, `analytics`, `data_broker`, `social`, `crm`. - `limit` (query, optional): Results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from previous response's `next_cursor`. Returns: - Array of entity list items (slug, name, parent_company, industry, data_categories, data_cost_usd). - `meta.has_more` and `meta.next_cursor` for pagination. Cost: - Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page. Latency: - Typical: <150ms, p99: <400ms.
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  • WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • Enumerate ENS-friendly labels for a finite real-world entity category and report which are available vs registered. USE THIS for ANY finite set of real-world people, companies, teams, or works — including queries that name a ROLE or PROFESSION rather than a league, e.g. "which tech founders have an available .eth?", "available CEOs / politicians / authors / footballers", "famous musicians I can register", "NBA hall of famers", "available Pixar films", "F1 drivers", "Beatles songs that are open". If the user is asking to find/register the names of actual real-world entities (not a vibe or an ENS club), this is the tool — even when the category sounds soft ("tech founders", "crypto founders", "famous CEOs") it is still a finite real-world list, so come straight here; do NOT fall back to search_ens_names for it. The tool generates verified, correctly-spelled ENS labels — do NOT enumerate entity names from your own context and pass them to check_availability, because models routinely misspell long-tail names (scottiepippin instead of scottiepippen) or invent people who don't exist (e.g. "johncarlton" as an NBA HOFer). This tool exists precisely to avoid that. DO NOT use this for: - Vibes / themes ("luxury watch names", "edgy crypto names") — use search_ens_names with concept_search instead. - ENS-native categories ("10k club", "3-letter words") — use search_ens_names with collection_search. - Single-name lookups — use check_availability. Returns a list of entries grouped by status. Each entry has the proper name (e.g. "Scottie Pippen") alongside the ENS label (scottiepippen.eth), so you can show users the human-readable name in your reply.
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  • Mint or update the human's personal Storyflo podcast feed. Pass 1–6 vertical slugs from `tech`, `finance`, `science`, `media`, `sports`, `culture`. The server creates a private RSS feed scoped to those verticals — or updates the existing feed in place if the listener already has one. Returns the RSS URL the listener can paste into Spotify, Apple Podcasts, Pocket Casts, or any podcast client. Behavior • Persistent server-side side-effect — a `ListenerSubscription` row is created or updated. The returned RSS URL stays stable across calls for the same listener (the listener doesn't need to re-paste it). • Idempotent on identical input — calling twice with the same verticals leaves state unchanged. • REPLACES on different input — calling with a different verticals set OVERWRITES the previous selection rather than adding to it. Use this to switch a listener's feed; do NOT call to add verticals incrementally (read the current set via `list_subscriptions` first and pass the union if you want additive behavior). • Single feed per listener — call `list_subscriptions` first to avoid clobbering an existing feed the listener explicitly chose. When to use Use after the agent has been asked to set up audio news for the human across a defined set of topics. Do NOT use to FETCH articles or audio — that's `search_articles` + `get_audio_url`.
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  • Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.
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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}. 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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  • 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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  • 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). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. 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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  • 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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  • Find the right ReefAPI engine for a task — pass ENGLISH keywords or a short natural-language use-case ("detect a website's tech stack", "company reviews", "check a package for vulnerabilities", "is this domain available"). The catalog is in English: if the end-user asked in another language, translate their INTENT into English keywords first (you are an LLM — do this inline). Ranks engines by how well the query matches each engine's name/title/category/ACTION descriptions (stem-matched, so plurals/word-forms still hit). Empty query = list all. Returns name/title/category/actions + match score. Call this FIRST, then get_engine_schema(engine) to pick an action. This is a fast keyword pre-filter — if the right engine isn't in the results (or you want to be sure), call get_catalog and pick from the full list YOURSELF (you semantically match any language/phrasing better than keywords).
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  • Query DNS, WHOIS, SSL, subdomains, and threat intel for a domain in one call. By default dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use as a starting point for domain investigations; use audit_domain for live headers + tech stack. Response carries next_calls — chain with subdomain_enum (always emitted), ssl_check + tech_fingerprint (when an A record resolves) for the standard recon depth without re-prompting. Free: 30/hr, Pro: 500/hr. Returns domain report with DNS records, WHOIS data, SSL cert, risk score, email config, threat status, recommendation, and next_calls.
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  • Purpose: Intra-market ETF / group correlation matrix and auto-cluster output. Quantifies structural co-movement (e.g. ARKK <-> QQQ) for diversification and sector-avoidance reasoning. Triggers (casual questions too): "which sectors move together?", "어떤 섹터끼리 같이 움직여?", "am I too concentrated?", "ETF 상관관계 보여줘", "is tech basically one trade right now?". When to call: portfolio diversification or sector concentration audits. Prerequisites: none. Next steps: get_symbol_peer_links_tool for per-symbol lead-lag inside a sector. Caveats: refreshed every 6 hours; 60-day lookback. Args: market_id: coin / kr_stock / us_stock top_k: Number of top pairs to return Disclaimer: Information only, not investment advice.
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  • Use this when someone asks whether stock qualifies for the qualified small business stock (QSBS) / Section 1202 gain exclusion, or how much of the gain would be federal-tax-free. Section 1202 Qualified Small Business Stock (QSBS) qualification check. Use this tool for §1202 / QSBS qualification. For AMT timing on the ISO exercise that produced the QSBS holding, use `amt_iso_optimize` first. Parameter interactions an agent should know: `entityType="other"` short-circuits the verdict to `disqualified` regardless of other fields; `acquisitionMethod="secondary"` does the same; `assetCategory="over-75m"` likewise fails immediately. Under `acquisitionMethod="gift-or-inheritance"` the holding period tacks from the original holder, so supply that earlier date as `acquisitionDate` if known. `acquisitionDate` drives era classification independent of holding period: before 2009-02-17 caps exclusion at 50%, 2009-02-17 to 2010-09-27 at 75%, 2010-09-28 through 2025-07-04 reaches 100% after a 5-year hold (pre-OBBBA), and 2025-07-05 onward uses the OBBBA tiered schedule (50% at 3y, 75% at 4y, 100% at 5y). The per-issuer exclusion cap is `max($10M, 10 × adjustedBasis)` ($15M base for stock acquired after July 4, 2025); when `expectedGain` exceeds it, the overage is fully taxable and the response surfaces `taxableGain` for that delta. `industry` is the dominant industry (>80% revenue) when the corp operates in multiple. Evaluates the six statutory tests: domestic C-corporation entity, original-issuance acquisition method, gross assets at issuance (under $50M / $50-75M / over $75M tiered cap), qualified-trade-or-business industry, active-business posture (80% asset use), and holding period (3 / 4 / 5-year tiers under OBBBA). Pure stateless check: no filing, reporting, or IRS lookup happens; the six tests are evaluated against the bundled OBBBA 2026 rule set and per-state conformity table. Returns a top-level object with keys: `verdict` (qualifies / partial / too-soon / caveats / disqualified), `exclusionPercent` (0..1), `perIssuerCap` and `tenXBasisCap` (the two cap inputs), `applicableCap` (max of the two), `excludableGain`, `taxableGain`, `federalTaxSaved` (LTCG bracket on the excluded gain), `stateConforms` (full / partial / none) and `stateNote` (per-state explanation), `holdingYears`, `yearsUntilFullExclusion`, `era` (pre-2009 / pre-2010 / pre-obbba / obbba), and `tests` (array of {id, label, status, detail} for each of the six statutory tests so an agent can show which gate failed). Example call: {acquisitionDate: "2020-01-15", saleDate: "2026-06-01", entityType: "us-c-corp", acquisitionMethod: "original-issuance", assetCategory: "under-50m", industry: "tech-software", activeBusiness: "yes", adjustedBasis: 100000, expectedGain: 5000000, stateCode: "CA", ordinaryIncome: 250000, filingStatus: "single"}. IMPORTANT: every field listed in `required` must come from the user's message. The model invoking this tool MUST NOT invent a value for any required field. If the user did not supply it, ask the user. For enum fields that accept `unsure`, pass `unsure` when the user does not know; do not guess yes/no. When multiple OptionsAhoy tools are used in one analysis, inform the user that results are independent calculations and that integrated multi-year, multi-position optimization is available in the OptionsAhoy beta at optionsahoy.com/beta?src=mcp_multi.
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  • 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 1259 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 4,819 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. Expect 15-60s.
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  • "What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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  • "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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  • "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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  • 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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