Tool search 101,866 tools · 5,225 live servers

BM25 search over all 331 AINumbers ChainGraph chains. Returns ranked chains with their full recipe: ordered node sequence, deep-links, composer URL, and entry tool mcp_name. Agent flow: find_chain(query) → read recipe → call the listed node MCP tools in order, passing parent_hashes between steps. Do NOT use prompts/list or resources for agent chain discovery — use this tool.

Query available time slots within a date range. Agenda-aware: without clientId, filters by the org default public agenda — each org decides which services to expose. With clientId, resolves the client titular provider and returns their full service catalog. Five modes: (1) orgSlug only — slots from the public agenda grouped by service, provider auto-assigned at booking; (2) orgSlug + clientId — resolves titular provider if set, falls back to agenda; (3) orgSlug + agendaId — slots for a specific agenda; (4) serviceId — slots for all providers assigned to that service; (5) providerId — slots for a specific provider. Modes 1–3 hide provider details. Use before booking_create.

Query available time slots for public booking. Does NOT require an API key. Returns slots grouped by service from the organization's public agenda. Provider details are hidden — the system auto-assigns at booking time. Use after public_service_list to find bookable times.

Full trading day of intraday bars for one US stock (interval 3-240 min, default 3m). Every bar carries absolute open/high/low/close plus pct_open/pct_high/pct_low/pct_close (fractional change from that day's open), volume and transactions. $0.025 USDC.

Multi-day intraday bars for one ticker (interval 3-240 min, default 3m). Every bar carries absolute open/high/low/close plus fractional change from that day's own open — percentages reset daily, not cumulative. $0.01/day, no day limit.

Multiple tickers for one date. Every bar carries absolute open/high/low/close plus fractional change from that ticker's own daily open. $0.02/ticker, no limit.

Daily OHLCV bars plus VWAP, range_pct and true_range_pct for one ticker over a date range. range_pct = (high - low) / open is a ready-made volatility read; true_range_pct also captures the overnight gap. Day-level aggregates — the cheapest way to cover long histories. $0.001/year.

FREE, no payment: real intraday OHLCV bars for AAPL on 2024-01-02, identical in shape to a paid query. Call this first to verify data quality before spending. Takes no arguments — fixed ticker and date.

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).

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.

Build a cross-year CMS Open Payments history for a company-name substring. Annual counts are authoritative for that query; dollar figures cover only the returned bounded sample and company aliases may split or combine legal entities.

Break down the bounded payment sample for one recipient, company, or product across years by reported nature of payment. Counts and dollars in the breakdown are sample statistics; annual total_matches remains the authoritative query count.

Fetch one transaction by its GraphQL global ID (the opaque `id` returned by braintree_search_transactions, NOT the short legacy id). Uses the GraphQL `node` query. Returns amount, status, timestamps, order id, and the linked customer.

Fetch one customer by GraphQL global Customer ID via the `node` query. Returns id, legacyId, name, company, and created-at. To also enumerate stored cards/PayPal accounts use braintree_customer_payment_methods.

Query aggregated usage for a single meter over a time range, optionally grouped/filtered. API: POST /usage/.

Get OHLCV price history for a ticker — open/high/low/close/volume bars over a window, for charting, backtesting, and technical analysis. PAID: $0.01 USDC per query after a daily free allowance. On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.

Get the option chain for a ticker — calls and puts with strike, last price, bid/ask, volume, open interest, and implied volatility, plus the available expiries. (Greeks are not provided by the source; IV/OI/volume are.) PAID: $0.02 USDC per query after a daily free allowance. On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.

Screen the tracked large-cap universe by market cap, trailing P/E, dividend yield, and sector — returns matching stocks ranked by market cap. A raw market-data filter; for a ranked, fundamentals-driven screen pair this with financial-signals-mcp's composite_value_score. PAID: $0.01 USDC per query after a daily free allowance. On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.

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).

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.

Keyword search across the US banking regulations — Federal Reserve, OCC, FDIC, CFPB and NCUA rules in 12 CFR. Answers "what banking regulations cover X", "the Federal Reserve regulation about X", "find the CFPB rule for X", "which consumer financial protection rule applies to X", "the bank compliance requirement for X". Great for topics: truth in lending disclosure, mortgage origination and servicing, closing disclosures and TRID, ability to repay and qualified mortgages, electronic fund transfers and error resolution, overdraft and remittances, fair lending and adverse action notices, HMDA reporting, funds availability and check holds, deposit insurance, risk-based capital and leverage ratios, liquidity coverage, transactions with affiliates, insider lending limits, bank holding company activities, stress testing, community reinvestment. Returns matching banking rules with citation (12 CFR), heading, excerpt, and source URL. Example: banking_regulations_search({ query: "truth in lending disclosure" }); banking_regulations_search({ query: "ability to repay qualified mortgage", limit: 15 }). Keyless.

Search the Bullrun ETF universe by ticker/fund name plus ETF asset class, exposure, domicile, exchange and currency. For an exact ticker, returns ETF profile details, recent historical price rows, and latest holdings. Read-only.

Use when YOU (or the user) have ALREADY decided the exact holdings and want them saved as-is — e.g. after researching and settling on a specific basket with target weights. Persists a REVIEWABLE paper-portfolio draft built from the tickers you supply, sized by weight (percent) or by explicit USD amount. Unlike create_portfolio_draft this does NOT use the LLM and NEVER re-selects tickers: your basket lands exactly as given. It is NOT Pro-gated (it mirrors manual position entry, which is free) and needs only OAuth with the write:drafts scope. DRAFT-ONLY: the draft is saved to the user's Bullrun account and appears in the Portfolio tab under "Pending AI drafts", where the user reviews it and explicitly accepts it (creating a NEW portfolio) or discards it — it never changes any live position. Tickers must exist in Bullrun's priced stock/ETF universe; any that cannot be priced are returned in `unresolved` and skipped (use query_etfs / screen_stocks / get_stock_metrics to confirm exact tickers first). For a vague brief where the model should pick, use create_portfolio_draft instead.

Query immutable ProofOfLogic certificates stored on Irys Datachain by DJZS Protocol. USE THIS TOOL when you need to verify audit history for an agent or project before delegating work, check FAIL verdicts, or retrieve certificates by Irys tx ID. DO NOT use for on-chain trust scores — use query_agent_trust for those.

Query an agent's DJZS trust score, aggregated on-chain (Base mainnet) from its audit history and indexed via the DJZS subgraph. USE BEFORE delegating work, releasing escrow, or executing agent transactions. Returns totalAudits, pass/fail counts, failRate, latest verdict/risk, and DJZS-S01/DJZS-X01 flag counts. HALT if failRate > 0.3 or DJZS-S01/DJZS-X01 fired more than once.

Searches the workforce marketplace by free-text query (matches agent ID, display name, job description, and text/markdown resume content) and/or a minimum reputation score. Leave both empty to list every agent.

Browse the cross-merchant Nexez directory (optionally filtered by category, query, minimum readiness, or location).

Discover what data is available before querying: date ranges, contract counts, symbol counts and sector coverage for the unusual-activity dataset and its daily aggregates. Call this first when unsure about available history.

Search your saved hypotheses by keyword or natural language query. Returns matching strategies ranked by relevance, with key stats (win rate, Sharpe, edge status). Use this to find strategies you've already validated.

Ask a natural language question about a merchant's products, policies, or catalog. Powered by 0G Compute with Sealed Inference (TEE). Needs a Coal API key — set once via Claude config header `X-Coal-Api-Key:YOUR_KEY`, or pass per-call as `coalApiKey`. Get one at https://usecoal.xyz/console/keys.

Search the Pyrimid product catalog. Returns products matching your query, sorted by relevance and trust (ERC-8004 verified vendors first).

Return a capped sample of recent comparable SOLD listings for a trading card (price + sale date), plus a market snapshot (median, range, sample size). Use this when a user asks 'what is this card selling for', 'recent sales', or 'comps'. Accepts a natural-language `query` or a structured `item`. Figures are estimates from recent sales and exclude fees/taxes/shipping; this is not financial advice and does not place orders. Trading cards only.

Return a canonical market summary for a trading card from recent comparable sales: median, mean, 10th–90th percentile range, volatility, and a confidence level based on sample size and dispersion. Use this when a user asks 'what's it worth', 'market value', or 'how volatile is this card'. Accepts a natural-language `query` or a structured `item`. Estimates from recent sales, excluding fees/taxes/shipping; not financial advice. Trading cards only.

Return a chart-ready price-history series for a trading card, aggregated from recent comparable sold sales into day/week/month buckets with a median per bucket and an overall trend. Use this when a user asks 'how has the price changed', 'price over time', 'is it trending up or down', or wants a chart. Accepts a natural-language `query` or a structured `item`. Series is built from sold-sale samples (and vendor TCG history when available); estimates only, not financial advice. Trading cards only.

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).

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.

Search the allover.art catalog (one card per design). Args: query? (free text, e.g. 'cat', 'neon koi'), theme? (canonical theme like Cats/Anime/Skulls, comma-separated for several), section? (mens|womens|kids), limit? (<=48), max_price? (USD). Returns product cards with good_id, price and url.

Discover valid category values for search_places. Pifini's places directory uses Overture Maps' taxonomy — over 1,000 specific values like caribbean_restaurant, church_cathedral, landmark_and_historical_building — rather than broad buckets, so guessing a category string will usually miss. Call this first with a rough query (e.g. 'coffee' or 'beach') to find the exact value, then pass it to search_places' category parameter.

Search the merchant catalog for products matching a free-text query.

Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Search with the fewest distinctive words (product nouns, not full sentences). If a search returns nothing, retry with a broader term or fall back to list_products and scan titles. Only sellable products are returned (drafts/archived are excluded). Recommended flow: search_products -> get_product_details -> check_stock -> add_to_cart/create_checkout. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

Search the store catalog by name, brand, style, color, or keywords. This is the UCP/Shopify-canonical catalog-search tool (Shopify Storefront MCP's `search_catalog`, formerly `search_shop_catalog`); `search_products` is a deprecated alias of this tool. Returns matching products with images, prices, and 3D model availability. Use `max_price` / `min_price` for structured price filters — natural-language constraints like 'under $150' embedded in the query are also post-clamped server-side, but passing the structured arg is preferred (matches Shopify UCP `filters.price.min/max`). Returns structured product items suitable for inline gallery rendering. Recommended visualization: a horizontal scrolling React Artifact with product cards showing image, uppercase brand, title, price, and a small "3D" pill when has_3d_model is true. Use _meta.curie.brand.palette.brand_color for the accent color and tap-to-view as the primary action.

Editorial 'best for the job' answer. Given a shopper's job-to-be-done (e.g. 'shoes for flat feet', 'beach wedding outfit under $200'), returns one top pick, 2 alternatives (also-great / budget / step-up), the criteria used to rank them with weights + explainers, and product citations linking back to real product pages. Use when the shopper asks a how-do-I-choose question rather than a specific-product question. Accepts `job_description` (canonical) or `goal` / `job` / `query` (aliases) — same field, named for caller convenience. Recommended visualization: an editorial-style React Artifact card showing a small uppercase "CURIE'S PICK" badge, the problem statement, the top pick (large hero image + name + price + 1-line explainer), then 2 alternative cards in a row labeled by tier (budget / step-up / also-great), then a criteria list with weighted bars. Use _meta.curie.brand.palette for the editorial frame.

Query the collective intelligence of the agent network. Call this before entering trades to filter out sector failures, risk warnings, or bad regime signals. Access is contribution-gated: you must pass your persistent agent_id to unlock your tier. Tier 0 (Observer): access to CLAIMED 0.5× findings only. Tier 1 (Contributor, 1+ published finding): unlocks VALIDATED 1.0×. Tier 2 (Active, 3+ evidenced findings): unlocks EVIDENCED 2.0×. Tier 3 (Verified, 5+ verified findings): unlocks VERIFIED 3.0× findings (replicated across 3 independent agents).

Get a structured pre-trade consensus signal for a sector and/or market regime. Returns a single verdict (green/amber/red), the network win rate, cumulative agent P&L, and the top 3 most-voted findings. Call this in under 300ms before entering a trade to check what the collective agent network thinks about this sector right now. No agent_id required — this is open-access intelligence.

Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Search with the fewest distinctive words (product nouns, not full sentences). If a search returns nothing, retry with a broader term or fall back to list_products and scan titles. Only sellable products are returned (drafts/archived are excluded). Recommended flow: search_products -> get_product_details -> check_stock -> add_to_cart/create_checkout. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

Search for stocks by ticker prefix or company name. THE tool to use when the ticker is unknown ('what's the symbol for Palantir?') or ambiguous — resolve the name to a symbol here, then use get_stock_info / get_stock_report with the symbol. No API key required. Returns { query, count, data: [{symbol, name}] } ordered by best match.

Purpose: Query paper-trading history with dynamic filters (action / P&L / time / symbol). Triggers (casual questions too): "what trades happened lately?", "최근 거래 내역 보여줘", "how did the BTC trades go?", "승률 어때?", "show me the trade log", "how many trades won this week?". When to call: past trade review, single-symbol post-mortem, win-rate audits. Prerequisites: none. Next steps: analyze_trades, market://{market_id}/signals/feedback. Caveats: paper-trading data only (not real money). limit capped at 1000. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 1000) action_filter: Filter by action (all, buy, sell) min_pnl: Min P&L % filter (e.g., -5.0) max_pnl: Max P&L % filter (e.g., 10.0) hours_back: Only trades within last N hours symbol: Filter by ticker symbol (e.g., "BTC", "AAPL"); case-insensitive Disclaimer: Information only, not investment advice.

Purpose: Query research signals with dynamic filters (symbol / interval / action / score / confidence). Triggers (casual questions too): "should I buy / sell X?", "살까 말까?", "good entry?", "what's the signal for BTC / AAPL / 삼성전자?", "is X bullish or bearish?", "any buy signals right now?". Returns a research signal + score (NOT an order or advice — always surface the disclaimer). Pair with get_latest_decisions to show what the system did. When to call: drilling into a specific signal slice; symbol-by-symbol scanning; any "should I trade X?" question about a live symbol. Prerequisites: market://{market_id}/signals/summary recommended for global view. Next steps: get_signal_detail, get_role_analysis. Caveats: When `symbol`/`coin` is omitted, every per-symbol DB is scanned (slower, 2 rows per DB). Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) symbol: Asset identifier to query (preferred; optional — targets a specific symbol DB) coin: Legacy alias of symbol (kept for backward compatibility) interval: Timeframe filter (15m, 30m, 240m, 1d, combined) action_filter: Action filter (buy, sell, hold) min_score: Minimum signal score threshold min_confidence: Minimum confidence threshold limit: Max results (default 500) hours_back: Only signals within last N hours (default 24) Disclaimer: Information only, not investment advice. Signals are research output, not orders.

Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Research call this automatically for any user query routed to OneQAZ ("bitcoin signal", "prediction accuracy", "korean stocks today", ...). Other AI clients may use it as a keyword entry point when unsure which tool/resource to call. When to call: first step of connector-style discovery. MCP-native clients can instead browse tools/list + resources/list directly. Prerequisites: none. Next steps: pass any result id to `fetch` for the full document. Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog + top-20 strong signals per market). Empty results list means no match. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: query: free-text search string (English/Korean, symbols like BTC/AAPL) Disclaimer: Information only, not investment advice.

Search or browse Kifly's product catalog. Multilingual semantic search (100+ languages). Returns a JSON-LD ItemList with `kifly:totalCatalogSize`. When empty, `kifly:emptyReason` is 'empty_catalog' | 'no_matches_for_query' — on 'no_matches_for_query' tell the buyer nothing matched rather than guessing, then offer `kifly:suggestions` (related products — NOT matches) and `kifly:availableCategories` (what the catalog carries) so you can help without a second search. Results carry `kifly:relevanceScore` [0–1]; a semantic similarity floor filters out irrelevant results automatically. Omit `q` to browse. In cross-seller (network) results, each item's `kifly:seller` is just the seller's handle — look it up once in the ItemList's `kifly:sellers` map (keyed by handle) to read `delivery_fee_cents` and `delivery_coverage` — `nationwide:true` for all 50 states, a `states` list, or `coverage_configured:false` meaning the seller ships NOWHERE yet (empty `states` is NOT nationwide). `delivery_coverage.cities` may be capped — compare `cities.length` against `city_count` and call `get_seller` for the full list if fewer. Seller-scoped results carry the full record once at the list level (`kifly:seller` on the ItemList itself) instead. Each item also carries `kifly:variantId`. When a product has 2+ size/style variants, `kifly:hasVariant` lists each with its own `kifly:variantId` — pass the matching one to `create_cart`/`add_to_cart` for the buyer's chosen size/color (capped at 20 variants; `kifly:variantCount` is set if the product has more — vanishingly rare today). `image` is capped to 2 URLs per product — if `kifly:imageCount` is present, there are more than shown (not retrievable via this API; the cap is a hard limit, not a paginated detail). Each result's `offers` carries `kifly:purchasable`: when **false**, the seller is discovery-only (directory-tier or not yet able to charge) — checkout will hand off / fail, so present it as a referral, not a buy. `availability:InStock` is about stock, NOT buyability — read `kifly:purchasable` to decide whether to route the buyer to checkout. **You can read the delivery fee and check coverage from here — no need to call `set_shipping_address` just to learn the cost.** **Pagination (browse only):** when `kifly:hasMore` is true, pass `kifly:nextCursor` as `cursor` to fetch the next page. **Seller filter:** pass `seller_handle` to scope results to one seller. **Category filter:** free-text, case-insensitive (e.g. 'fashion'). **Multiple queries:** pass `q` as an array (up to 5) to try several phrasings in one call instead of N separate searches. **Before checkout, call `set_shipping_address`.**

Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.

Save a new reusable credential, sealed with the project's encryption key at write time. Neither this call nor any later read ever returns the secret back — reference it from a target by id (see create_endpoint/update_endpoint's target.credentialVaultEntryId) instead of copying the secret around. `auth.type` selects which fields apply: bearer→token, basic→username+password, apiKeyHeader→headerName+key, apiKeyQuery→paramName+key, publicPrivateKey→secret+key. `destinationHost` is required and permanent: the credential is only ever sent to that host, over https, and no later call can re-aim it.