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298,188 tools. Last updated 2026-07-14 11:30

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  • Creates an automation on a perspective. Triggers: per_interview (fires on every completed conversation) or scheduled (daily/weekly digest). Channels: webhook, email, or connected provider-backed integrations such as Slack, HubSpot, Gmail, Google Docs, Notion, and Confluence. Execution modes: direct (fast, deterministic, webhook-only) or agent (LLM-powered for email and provider-backed channels). Behavior: - Each call creates a new automation — even if name/config matches an existing one. - Once enabled, the automation starts firing on real events: per_interview sends on every completed conversation going forward; scheduled sends a real message on the configured cadence (daily/weekly). - For HubSpot, the workspace's HubSpot connection is required — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if not connected. - Webhook channels: do NOT ask the user for the endpoint URL or credentials — neither is accepted through this tool. The automation is created disabled and the response includes configure_url, a web app page where the user sets the URL (and an authentication header if needed). Share that link and ask the user to reply "Done" after saving, then enable the automation via automation_update. - Errors when the perspective is not found or you do not have access. When to use this tool: - The user wants ongoing notifications on every completed conversation (per_interview). - Building a daily/weekly digest delivered to Slack, email, HubSpot, or a webhook (scheduled). When NOT to use this tool: - Trying a one-off send before going live — create the automation, then use automation_test (use override_email on email channels to avoid hitting real recipients). - Editing or toggling an existing automation — use automation_update. - Connecting Slack or HubSpot — use integration_manage first; the provider must be connected before slack/hubspot channels work. Example — per-conversation Slack notify (resolve the channel with slack_channel_resolve first, then pass it as resource_id): ``` { "perspective_id": "...", "automation": { "name": "Notify Slack", "trigger": { "type": "per_interview" }, "execution_mode": "agent", "channel": { "type": "composio", "delivery_config": { "provider": "slackbot", "tool_slug": "SLACKBOT_SEND_MESSAGE", "resource_id": "C0123ABCD", "resource_name": "#research" } } } } ``` resource_id is the Slack channel ID or name. The channel is re-verified live on create; an unresolvable channel is rejected. Typical flow: 1. integration_manage (operation: "list"/"connect") → ensure Slack / HubSpot is connected (only needed for those channels) 2. For Slack: slack_channel_search / slack_channel_resolve → find/verify the channel to use as resource_id 3. automation_create → create the automation 4. automation_test (with overrides) → verify delivery before relying on it
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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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  • 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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  • What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).
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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 1293 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 4,927 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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  • "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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  • Confluence MCP — wraps the Confluence Cloud REST API v2 (OAuth)

  • Connect to Atlassian Jira, Confluence, and Compass to search, create, and manage your work.

  • "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. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
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  • Run a natural-language analytics question against your connected data sources. Consumes AI credits. Returns either the completed analysis result inline OR a job_id you can poll with get_analysis_status. If list_data_sources returns an empty list, ingest data first with upload_data_source (inline base64), ingest_url_data_source (public URL), or request_oauth_integration_url (Google / Meta / Jira / Confluence).
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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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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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  • Institutional-grade BUY/SELL/HOLD directive for US equity symbols — the production-grade upgrade from demo_council (which is IWM-only, 5-min cached, free). Aggregates 8 proprietary engines — gamma-flow + flip detection, VPIN order-flow toxicity, fractal anchor confluence, regime classifier, dark-pool axis tracking, options sweep intelligence, mean-reversion regime, and Battle Computer consensus — into one tradeable verdict: directive, confidence 0-100, regime label (ALPHA_EXPANSION / MACRO_COLLAPSE / NEUTRAL / SHIELD), price targets (tp1/tp2/stop), and a per-engine breakdown explaining the score. Call this when you need a high-conviction directional read before sizing or executing a position — this is the same verdict institutional desks subscribe to at $1,000/mo via the Leviathan tier. Cost: 0.10 RLUSD per call (~$0.10). 60-second per-symbol cache, so back-to-back queries on the same ticker are effectively free. Pass payment_token from verify_payment plus your agent_wallet. Coverage: US equities; crypto coverage in roadmap. Typical response time: <2s cached, ~4s fresh compute.
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  • Get today's AI-generated commentary for all 10 analysts — 2-sentence summaries with signal count, confluence score, BTC trend (generated 9:00 UTC) — Returns today's AI-generated daily commentary for all 10 analyst personas. Summaries are generated each morning at 9:00 UTC using gpt-4o-mini based on the previous 24h of signals, Fear & Greed score, BTC trend, and cross-analyst confluence. Public endpoint — no authentication required. Only shortSummary is returned (2 sentences). Full commentary is available via the authenticated Pro endpoint /api/analysts/:id/daily-summary. Fields per analyst: analystId, analystName, summaryDate (YYYY-MM-DD), shortSummary, signalCount (signals in the past 24h), confluenceScore (0-100: % of other analysts with overlapping tokens in last 2h), fearGreedScore (0-100), btcTrend ('up'|'down'|'sideways'|null). Cached 30 minutes. Returns empty summaries array before 9:00 UTC on any given day. 60 req/min rate limit.
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  • Initiate an OAuth handoff to a vendor integration (Google Ads, GA4, Search Console, Sheets, Drive, BigQuery, Meta Ads, Jira, Confluence). Returns an authorization URL the user opens in a browser. After the user clicks Allow, the connection is created and you can poll check_integration_status(handoff_id) to find out when the data is ready.
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 4,927 across 1293 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.
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  • Where Hyperliquid smart money is positioned. Filters the HL leaderboard to consistent directional traders (excludes market-makers + dust), then aggregates their live positions into a per-coin consensus (long/short counts, net notional, bias, conviction) plus a top-trader drill-down. Optionally focus one coin via `market`. A positioning signal, not a trade — use as confluence/risk context, not a standalone entry.
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  • Resolve a pharma sponsor / manufacturer name (e.g. 'Janssen Pharms', 'AstraZeneca AB') to its public ticker, so an FDA approval / trial readout / recall can be joined to 13F ownership, insider (Form 4) buys, and the confluence signal. Returns ticker, company name, exchange, and resolution method (alias|exact|fuzzy|unresolved). Returns ticker:null for private sponsors or unmatched names rather than guessing — joins must never key on a wrong ticker.
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  • For ONE ticker: do the independent disclosure sources AGREE? Returns the cross-source confluence verdict — institutions (13F) × insiders (Form 4) × Congress (STOCK Act) all buying/selling the same name — PLUS the capital-rotation context: which sector the ticker is in and whether smart money is rotating INTO or OUT of that sector this quarter. The two flagship signals on one name. Congress is a free corroborating leg (never the sole basis). PRICE-FREE; NOT investment advice.
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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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  • 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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  • Top Gainers (Alpha-Ranked): Today's biggest 24h gainers across the CoinGecko top-1000 universe (stablecoins and wrapped BTC/ETH derivatives filtered out) plus per-chain ecosystem gainers for Ethereum, Base, Arbitrum, BNB Chain, Polygon, and Optimism — RANKED BY CROSS-SIGNAL ALPHA CONFLUENCE, not raw % move. Each gainer is scored by how many independent signals corroborate it (whale accumulation, cross-chain DEX trending, social/CT hype, sector rotation, multi-timeframe chart trend, AI forecast); tokens with real confluence lead and pure price-pumps are demoted. Returns: alphaRanked[] (symbol, verdict, signalCount, per-signal badges + evidence), rawMovers[] (no-confluence pumps), losers, and per-chain breakdown. Optional { params: { chain } } drills into one chain. Reads cached signals (zero extra API cost) with graceful degradation. Use to find what's actually worth attention, not just what's pumping.
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