"confluence" matching MCP tools:

• polymarket_arbitrageA

  ConfluenceB

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

  ConnectorNo auth

• kalshi_weather_edgeA

  ConfluenceB

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

  ConnectorNo auth

• research_rowA

  ConfluenceB

  Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).

  ConnectorNo auth

• pipeworx_feedbackA

  ConfluenceB

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

  ConnectorNo auth

• integration_manageA

  Perspective AIA

  Read-only inspector for workspace integrations. Operations: "list" enumerates the registered providers (currently slackbot, hubspot, gmail, googledocs, notion, confluence, salesforce, prodege) with connection status, a description and has_tools; "connect" returns a setup URL the user opens in a browser to complete OAuth, or, for providers connected with customer-issued keys (prodege), a page where the user pastes those keys; "search_tools" returns the available action slugs (e.g., SLACKBOT_SEND_MESSAGE, HUBSPOT_SUBMIT_FORM, GMAIL_SEND_EMAIL) for a connected provider that has tools. Behavior: - Read-only. Does NOT itself perform OAuth — "connect" just hands a setup URL back so the user can finish the connection in the web app. An already-connected provider still returns the setup_url: that page is also where the user reconnects with a different account (e.g. another Gmail sender). - Never ask the user for API keys or secrets in chat — they are only ever entered on the setup_url page. Share the link and ask the user to reply "Done" once saved. - Errors when the workspace is not found or you do not have access. - has_tools: false marks providers with nothing to search or automate. Panel providers (prodege) are such: the connection only sends participants back to the panel when a conversation ends, configured in the perspective's outline by the design agent (perspective_create / perspective_update). Do not call search_tools for them; it returns no tools either way. - Panel routing requires the panel connected first: when the user says participants come from a panel, check "list", and if the panel is not connected, run "connect", share the setup_url and wait for "Done" before creating or updating the perspective. - search_tools returns success: false with "No active <provider> connection. Use 'connect' operation first." when the provider is not connected. Limit is 10 tools per search. - Required params per operation: connect needs provider; search_tools needs provider and query. MCP schema validation rejects missing or unrelated operation arguments before execution; the handler retains equivalent errors for non-MCP callers. When to use this tool: - Checking which integrations the workspace has connected before configuring an automation that talks to one of them, or before designing a perspective that routes participants back to a panel. - Surfacing the setup URL to the user when they want to connect a provider. - Discovering action slugs to populate provider-backed automations. When NOT to use this tool: - Creating or modifying automations — use automation_create / automation_update after the provider is connected. - Sending a real message to test a provider wiring — create the automation first, then run automation_test. Examples: - List: `{ "operation": "list" }` - Connect: `{ "operation": "connect", "provider": "slackbot" }` - Search: `{ "operation": "search_tools", "provider": "hubspot", "query": "create contact" }`

  ConnectorOAuth

• automation_createA

  Perspective AIA

  Creates an automation on a perspective. Triggers: per_interview (fires on every completed conversation) or scheduled (daily/weekly, with digest, invite, or insights purpose). Actions: internal insight discovery, 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, required for insights and delivery 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. - Insight automations use kind "insights", execution_mode "agent", omit channel, and use scheduled purpose "insights" when scheduled. - 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). - Running scheduled insight automation which creates insights without external delivery. 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

  ConnectorOAuth

• backtest_strategyA

  TraderWAIA

  Backtest a rule-based strategy on TraderWAI historical 1-minute data and get every trade plus a summary (win rate, total R, drawdown). It uses the SAME rules and engine as create_conditional_order, so a rule that looks good can be turned into a live conditional order. Pure calculation on the server, no AI: use it whenever the user asks to backtest or "how would this have worked" — do not compute backtests yourself from get_ohlc candles. Levels can be daily references so the rule works every day: {"ref":"session_high","session":"asia"} (asia 00:00-07:00, london 07:00-16:00, new_york 13:00-21:00 UTC), {"ref":"range_high","start":"00:00","end":"06:00"}, {"ref":"prev_day_low"}; the rule is armed each day once its levels are known. Example — "buy EUR/USD when it breaks the Asian high": symbol EUR_USD, conditions [{"type":"close_above","timeframe":"M15","level":{"ref":"session_high","session":"asia"}},{"type":"time_window","start":"07:00","end":"12:00"}], trade {"side":"buy","stop":{"type":"level","ref":{"ref":"session_low","session":"asia"}},"target":{"type":"rr","value":2},"closeAt":"20:00"}. Example — "sell gold when it sweeps the Asian high": conditions [{"type":"sweep_above","timeframe":"M15","level":{"ref":"session_high","session":"asia"},"maxCandles":2}], trade {"side":"sell","stop":{"type":"wick","bufferPips":20},"target":{"type":"rr","value":2}}. Indicators and trend filters: any level can be {"ref":"indicator","indicator":"sma|ema|rsi|atr|highest|lowest","timeframe":"D1","period":50}, {"ref":"price"}, {"ref":"day_open"} or {"ref":"day_high","daysAgo":2}; combine them with compare (e.g. {"type":"compare","left":{"ref":"price"},"op":"above","right":{"ref":"indicator","indicator":"ema","timeframe":"D1","period":50}}) and candle_direction (e.g. yesterday bullish). Indicators use closed candles only. Example — "buy gold on the first retest of a bullish M15 FVG in London": conditions [{"type":"fvg_retest","timeframe":"M15","direction":"bullish","maxCandles":20},{"type":"time_window","start":"07:00","end":"12:00"}], trade {"side":"buy","stop":{"type":"wick","bufferPips":10},"target":{"type":"rr","value":2}} (the wick stop goes beyond the gap's far edge). Limit entries and time exits: trade.entry {"type":"limit","at":{"type":"fvg","point":"midpoint"},"expiryMinutes":60} waits for price to come back to the limit (unfilled signals are reported), trade.maxMinutes closes after N minutes, and the target can be "none" with a time exit. Example — "when a bearish M15 FVG forms on gold, sell limit at its midpoint, stop above the gap, 2R or close after 2 hours": conditions [{"type":"fvg_formed","timeframe":"M15","direction":"bearish"}], trade {"side":"sell","entry":{"type":"limit","at":{"type":"fvg","point":"midpoint"},"expiryMinutes":60},"stop":{"type":"wick","bufferPips":20},"target":{"type":"rr","value":2},"maxMinutes":120}. Example — "buy the break and retest of today's 08:00-10:00 UTC range high": conditions [{"type":"retest","timeframe":"M15","direction":"up","level":{"ref":"range_high","start":"08:00","end":"10:00"},"maxCandles":10}], trade {"side":"buy","stop":{"type":"wick","bufferPips":5},"target":{"type":"rr","value":2}} (the wick stop goes below the retest candle; same rule and result as a live conditional). A limit entry at a level can be shifted with trade.entry.at.pips (positive above, negative below the level). COSTS: costs {"spreadPips","slippagePips","commissionPerLot" (USD, gold/silver/USD forex) or "commissionPips"} are charged on buys AND sells and the result shows R gross and net (spreadPips alone works the same). Always add realistic costs for strategies with small stops (M1/M5). RESULT DETAIL: every trade has its stop size in pips, MFE and MAE in R (how far it went for / against before closing), and the summary breaks the result down by month, by halves of the period, by weekday and entry hour, with profit factor and how many trades reached +1R…+4R: use it to choose the target and stop instead of running one backtest per variant; allTrades:true lists every trade (up to 300). STOP AT THE GAP: trade.stop {"type":"fvg","edge":"far","bufferPips":30} puts the stop beyond the far edge of the fvg_retest/fvg_formed gap, and "edge":"pattern" beyond the high/low of the three candles that form it; with a limit entry at the far edge use "pattern" (a far-edge stop would sit right next to the entry). trade.minStopPips/maxStopPips skip signals with a stop outside that size (counted). MANAGEMENT: trade.management {"breakevenAtR":1,"partials":[{"atR":1,"closePct":50}]} moves the stop to entry at +1R and closes half at +1R. LOCAL MARKET TIME: time_window, range_high/range_low and trade.closeAt accept "timezone" (e.g. Europe/London, America/New_York), so a London 08:00-11:00 window is measured at the same market hour in summer and winter. MARKET STRUCTURE: swings, BOS/CHoCH, order blocks, flip zones (broken base), Fibonacci of the last impulse, FVGs and equal highs/lows are detected candle by candle without look-ahead (see the condition types). Example — "sell gold on the first retest of a bearish order block + Fibonacci 0.5-0.618 + broken base overlap on M15, rejection candle on M5, bearish day, stop above the zone, target the last swing low, exit if no new low in 6 M5 candles": conditions [{"type":"bos","timeframe":"M15","direction":"bearish"},{"type":"confluence","direction":"bearish","zones":[{"zone":"order_block","timeframe":"M15"},{"zone":"flip_zone","timeframe":"M15"},{"zone":"fib_zone","timeframe":"M15","from":0.5,"to":0.618}],"min":2,"trigger":"rejection","confirmTimeframe":"M5","maxTouches":3},{"type":"compare","left":{"ref":"price"},"op":"below","right":{"ref":"day_open"}}], trade {"side":"sell","stop":{"type":"zone","edge":"far","bufferPips":20},"target":{"type":"level","ref":{"ref":"swing_low","timeframe":"M15"}},"management":{"exitIfNoProgress":{"candles":6,"timeframe":"M5"}}}. Results are broken down by zone / combination. Selective setups give few trades: use long periods, and sensitivity:true reruns the rule with neighbouring parameters (swing strength ±1, target ±0.5R) to see if the result holds. TRAILING: trade.trailing true moves the stop behind price at the initial stop distance (same rule as trailing: true on live orders). Not supported: other chart patterns (head and shoulders, triangles…), news, ATR or candle trailing stops. History available: forex, gold and crypto since late May 2026; indices and oil about 1 year. Plan limits: Free = last 7 days and 5 backtests per day; Plus = 45 days and 20 per day; Trader = 90 days and 50 per day; Max = all history, unlimited. Works with the market closed (weekends) and without a broker account.

  ConnectorOAuth

• compare_entitiesA

  ConfluenceB

  "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 + operating income + 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.

  ConnectorNo auth

• validate_claimA

  ConfluenceB

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

  ConnectorNo auth

• company_factsA

  ConfluenceB

  TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and balance-sheet figures are NEVER derived. Every non-answer is a named status — `unavailable` (the filer did not report it for that period; the periods that DO exist are listed, without values), `unsupported` (outside what v1 covers — a non-us-gaap filer, an unknown attribute, a non-USD unit), `ambiguous` (the company name matched two filers equally well; both are named), `conflicting` (two filings the same day disagree; both are returned and neither is picked), `partial` (a value with no accession behind it). Source: SEC EDGAR, XBRL financial facts, one publisher read once — see `corroboration`. Same response is served at POST https://gateway.pipeworx.io/v1/facts for non-MCP callers.

  ConnectorNo auth

• company_register_searchA

  ConfluenceB

  Search the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want the OFFICIAL REGISTERED ENTITY and the country is not the US, or when you do not know which country it is. Registers searched: Companies House (GB), Enhetsregisteret (NO), PRH/YTJ (FI), Sirene (FR), 法人番号公表サイト (JP), RIK e-Business Register (EE), and the GLEIF LEI crosswalk, which reaches jurisdictions we have no register pack for (DE, SE, AU, SG...). THIS TOOL REFUSES TO GUESS, and that is its point: when two different companies match a name equally well it answers `match_status: "ambiguous"` with BOTH, rather than naming one. A one-word trading name usually IS ambiguous — "Bolt", "Apple", "Next", "Sea", "DNB" each match a well-known group AND unrelated micro-entities whose registered name is exactly that word — so pass `country` when you know it. Every response names EVERY register it asked under `searched[]`, with that register's own as-of date and row count, and a register that could not be reached carries an `error` there rather than reading as zero rows: "we could not ask Companies House" is never the same answer as "this company is not in Britain". `related[]` holds entities that collapsed INTO the match — the same company seen through another register, or another country's arm sharing its LEI — so a multinational returns one answer rather than tying with itself. NOT A PEOPLE SEARCH: sole traders and other natural persons are excluded on the publisher's own legal-form field, never guessed from the shape of a name. For US public companies prefer resolve_entity (CIK + ticker + FIGI); the two share this same matcher, and resolve_entity returns these register fields too.

  ConnectorNo auth

• archive_evidenceA

  ConfluenceB

  What did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy, and the GAP between the date asked for and the capture actually used (e.g. "Captured 1.5 day(s) before the requested date"). One call replaces the find-capture, fetch-record, parse-HTML chain. If no archive holds a capture within max_gap_days (default 30) it answers status "no_capture" and names the nearest capture before and after the date with their gaps, so a distant capture is never passed off as the page on that date. Every archive's outcome is listed in sources[]; one that timed out or errored says so with its upstream status, and if all of them failed the answer is error "archives_unavailable", not no_capture. Captures are immutable and cached by capture id; served_from says whether the text came from cache or upstream. Use archive_compare to see what changed between two dates.

  ConnectorNo auth

• set_jira_connectionA

  KaikuA

  Switch a project's Jira connection on or off, keeping its token — and, with 'confluence', whether the same site's Confluence is read too. Off, nothing reads that Jira or writes to it — the issue screen, the agents, the status sync — and issues keep their keys there. Switched on, the held token is asked of the site first and refused with the site's reason if it no longer opens it. Connecting a Jira, or giving a new token — Confluence's own included — is done on the board's Jira sheet (get_jira_connection's 'url'), never here. For whoever administers the project.

  Connector

  Destructive

  No auth

• search_linked_confluenceA

  KaikuA

  Search the Confluence of a project's own Jira site (see get_linked_confluence_page) with CQL, as its own search does — e.g. 'type = page AND text ~ "onboarding" ORDER BY lastmodified DESC', 'space = ENG AND title ~ "release"', 'parent = 123456'. Answers each page's confluence_page_id, confluence_url, title, space, an excerpt and when it changed; read one whole with get_linked_confluence_page. A query the site cannot read comes back with its reason. This tracker's own wiki is searched with search_wiki. Name the project by 'project', or an issue here by 'issue'. For the people who work in the project; at most 10 reads of the linked site a minute per person.

  ConnectorNo auth

• updown_venue_compareA

  ConfluenceB

  The same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as ISO such as 2026-10-27T14:15:00Z, on a 15-minute boundary). Per venue: question, Up bid / ask / mid / last, spread_pp, quote_state, volume WITH ITS UNIT, open interest, liquidity, settlement source, comparison basis, when it settles. THE VENUES ARE NOT ONE CONTRACT. Kalshi and Polymarket US settle on CF Benchmarks' BRTI (60-second average at the end against the same at the start): the same bet, so a gap between them is a real disagreement. polymarket.com settles on a Chainlink 60-second TWAP, end against start: same comparison shape, different feed, so a gap against either is partly the contract. `pairs[]` gives equivalent ("true"|"false"|"unclear"|"not_comparable") from resolution_diff for every pair, material_differences, mid_diff_pp (a minus b, in percentage points) and read_as in words. Measured over 30 days of settled windows the Chainlink and BRTI feeds settle a window differently about 1.2-1.8% of the time, almost entirely windows that ended near the threshold (see updown_divergence_rate). `net_spread` is computed only for the pair that is equivalent: the gap in mid terms and at top of book (buy Up where cheaper at the ask, Down where Up is dearer at 1 minus the bid) after BOTH venues' taker fees, each fee carrying its verified-as-of date. Top-of-book size is shown where the venue publishes it (Polymarket US); the Kalshi figure is not in its market payload, so read its order book before sizing. A venue with no market for the window is {status:"not_listed", reason}; one that could not be read is {status:"unavailable", refusal} -- never an empty object, and never the same thing. A one-sided book (typical Polymarket US just before a window is decided) has quote_state "bid_only", a null mid, and the last trade stands in labelled price_basis "last". Quotes are read venue by venue (cached up to 60 seconds), so a gap of a couple of points on a moving window is inside the skew. Only the 15m horizon is offered: it is the one where all three list the same up/down question. For the settled history of how often the venues disagreed, call updown_divergence_rate.

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• openmeteo_get_floodA

  open-meteo-mcp-serverA

  GloFAS (Global Flood Awareness System) daily river discharge (m³/s) from the GloFAS 0.05° grid cell nearest the given coordinates — no river ID needed. The response's latitude and longitude name that cell. It is not reliably on the river intended: a point near a confluence, a pair of parallel channels, or a bank can resolve to a cell with little flow. When the value is far below what the named river carries, step the coordinate 0.05° across the channel and compare the values. One mode per call: forecast_days for the outlook, or start_date and end_date together for a date range from 1984-01-01. The two modes are mutually exclusive, and a date range needs both ends — a lone start_date or end_date is rejected. forecast_days accepts up to 210, but the current GloFAS run sets how far the data reaches (about six months ahead); later days come back null and the notice names where the data ends. What a date range holds depends on the model. Omitting models reads seamless_v4: consolidated_v4 reanalysis before 2023-08-05, then forecast-run discharge from 2023-08-05 on, so a range crossing that date changes source mid-series. Set models to pin a source: seamless_v4 (default — consolidated_v4 reanalysis before 2023-08-05, forecast_v4 runs from 2023-08-05 into the live forecast), consolidated_v4 (reanalysis from 1984 through 2025-05-31; null after it and on every forecast_days call), forecast_v4 (forecast runs from 2023-06-01 into the live forecast; null before). consolidated_v4 is the only way to read reanalysis for 2023-08-05 through 2025-05-31. With 2+ models every daily column is suffixed with the model name (river_discharge_consolidated_v4). Available daily variables: "river_discharge" (the daily discharge value; the mean across ensemble members is "river_discharge_mean"), and the ensemble statistics "river_discharge_mean", "river_discharge_median", "river_discharge_min", "river_discharge_max", "river_discharge_p25" (25th percentile), and "river_discharge_p75" (75th percentile). The statistics carry spread across ensemble members on forecast-run days — the forecast_days outlook, and date-range days from 2023-08-05 under seamless_v4 or forecast_v4. On reanalysis days (seamless_v4 before 2023-08-05, consolidated_v4 through 2025-05-31) they repeat river_discharge and are not uncertainty estimates. Returns null for coordinates far from any river or in areas without GloFAS coverage. A wide date range produces thousands of daily records and spills to a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled it returns a bounded preview instead.

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• scan_dependencyA

  ConfluenceB

  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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• polymarket_fill_riskA

  ConfluenceB

  Realizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the direction that loses money). 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, verdict (clean|degraded|cannot_fill), fee_schedule ({category, taker_rate, exponent, maker_rebate_rate, fees_enabled, basis} — read off THIS market, never assumed free), fee_usd/fee_pp at the actual VWAP fill, breakeven_win_rate_taker (the win probability your side needs just to cover price + fee — ~51.8% on a 50c crypto taker fill, not 50%), breakeven_win_rate_maker (no fee charged to makers — any rebate is in fee_schedule.maker_rebate_rate), and all_in_cost_pp = slippage_pp + fee_pp. 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 and profit_usd — both NET of every leg's own Polymarket taker fee (capture_ratio_gross/profit_usd_gross keep the pre-fee figures; per_leg[].fee_usd itemizes each leg, summed as fees_usd_total), 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). Adverse selection while working resting orders is still not modeled.

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• release_calendar_marketsA

  ConfluenceB

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns the SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" and a `hint` telling you to widen, so you never branch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• query_knowledgeA

  mcp-serverA

  Search the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited synthesized answers, lifecycle awareness, and refusal-on-weak-context. Returns a written answer with [n] citations plus the ranked source chunks. Modes: `fast` (1,500 kT — retrieval-only, no synthesis), `standard` (12,500 kT — default; synthesized answer over the top retrieval set), `deep` (25,000 kT — wider retrieval + premium synthesis for complex questions). Pick the cheapest tier that answers the question. Responses are capped at 25,000 output tokens per Claude Connectors policy; if truncated, structured metadata carries `truncated: true` and `query_id` so the agent can call `get_source_detail` for full provenance.

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• get_technical_indicatorsA

  TraderSpyA

  Technical analysis for a crypto futures symbol. 19 indicators: RSI, MACD, EMA, SMA, Bollinger Bands, ATR, ADX (+DI/−DI), Stochastic, OBV (with price divergence), VWAP, CCI, MFI, Williams %R, ROC, SuperTrend, Ichimoku, Keltner Channels, classic pivot points and swing support/resistance levels. Every indicator returns its current value, the previous bar, a rising/falling/flat direction, a short `series` history and — where it applies — zones and crossovers. EMA/SMA accept several periods at once (default 20/50/200); every period is overridable via `periods`. Pass `intervals` (up to 3) to get 1h/4h/1d in ONE call with a multi-timeframe `confluence` verdict. Each timeframe also carries a `summary` (bias, trend, momentum, volatility, volume, plain-language notes) computed from a fixed indicator set — quote its notes rather than the raw score. For several coins pass `symbols` (up to 3) instead of one call per coin: ONE call and one quota unit, one entry per symbol in `data`.

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