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460,071 tools. Updated 2026-08-17 15:54

"Woo" matching MCP tools:

  • Query data rows for a single WHO GHO indicator with optional spatial, temporal, and dimension filters. Returns rows with numeric values, uncertainty intervals (Low/High), and spatial/time metadata. This is the primary data-fetching tool in the find-then-query workflow: use who_search_indicators to find the indicator code, optionally call who_get_indicator_metadata to confirm which filter dimensions are valid, then call this tool. Spatial filters are mutually exclusive per call: provide only one of country_codes, region_codes, or income_group_codes — mixing them triggers an error. Omitting all spatial filters returns all geographies (may be large; use limit to cap). The sex filter only applies when the indicator uses SEX as its first cross-cutting dimension — if not, the filter returns empty rows; check who_get_indicator_metadata first if uncertain.
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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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  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
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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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  • 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 1461 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,558 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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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  • 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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Matching MCP Servers

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Matching MCP Connectors

  • Connect your Woop account to AI via Brazil's Open Finance: balances, statements, cards, investments.

  • ProWoDoOAuth

    Manage tasks, sprints and daily planning in chat. ProWoDo's connector lets Claude, ChatGPT and other AI agents create and update tasks, plan sprints, write daily standups and assign work via natural language. 50+ tools, OAuth 2.0 secured.

  • 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.
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  • Returns transactions for a bank account (BANK or CREDIT type). For CREDIT (credit card) accounts, this is the ONLY way to get itemized transactions (purchases, subscriptions, etc.). Each credit card transaction MAY carry `creditCardMetadata.billId` pointing at a bill from openfinance_list_credit_card_bills, but this is a per-connector HINT, not authoritative: some connectors (e.g. Nubank) populate it sparsely (many transactions and installments arrive with no billId) or inconsistently (the same payment tagged to more than one bill). Do NOT reconstruct a bill's total by summing transactions by billId — the bill's own `totalAmount` from openfinance_list_credit_card_bills is the source of truth. CREDIT PENDING vs POSTED varies by connector: where the bank exposes future-dated `status:'PENDING'` installments, those represent the OPEN bill plus future bills (future months); where it does NOT, only the last closed bill's POSTED items appear until ~closing. Same query, different coverage per bank (upstream). To get a standardized open-bill total / total debt regardless, use openfinance_list_credit_card_bills (`open_bill` / `total_pending_debt`). Supports from/to date filters (ISO YYYY-MM-DD) and an optional keyword filter via `search_queries` (case- and accent-insensitive substring match against description and merchant name, OR semantics across multiple terms). When `search_queries` is set the tool aggregates up to 5000 transactions within from/to before filtering — narrow from/to if `truncated:true` is returned. PAGINATION: OMIT both `page` and `page_size` (the default) to get ALL transactions in the from/to range in one call — the tool auto-paginates the upstream and returns them under a single logical page (`page:1`, `totalPages:1`), up to a 5000 ceiling (`truncated:true` + warning if exceeded, then narrow from/to). Passing `page` and/or `page_size` switches to MANUAL pagination: you get one page (`page_size` items, default 50, max 500; `page` defaults to 1) with the REAL `total`/`totalPages`, so `page_size:5` alone returns the first 5 with `totalPages` telling you how many pages remain. On upstream errors, returns { total:0, results:[], warning, error } instead of throwing. `detail` controls how much per-row data you get (default `'compact'` = slim, cheap). Use `detail:'rich'` to enrich each row (when the bank connector provides it) with `merchantInfo` (estabelecimento: businessName/razão social, cnpj, cnae, category — useful for auto-classifying spending) and extra `creditCardMetadata` fields: `billId` (a per-connector HINT toward the transaction's bill — sparse/inconsistent on some connectors like Nubank, so do NOT sum by it to get a bill total; use the bill's `totalAmount` instead), `purchaseDate`, `payeeMCC`, `feeType`/`feeTypeAdditionalInfo`, `otherCreditsType`/`otherCreditsAdditionalInfo`. Use `detail:'raw'` to get the FULL untouched Pluggy transaction object (everything Pluggy returns, un-normalized — heaviest, for when you need a field we don't project). 'rich'/'raw' add tokens per row and coverage varies by bank/Open Finance, so keep the default for normal listings. For the card's statement closing/due dates use openfinance_list_accounts (`creditData.balanceCloseDate` / `balanceDueDate`). The response opens with an `account` echo block ({ account_id, bank, name, number, type, item_id }) identifying WHICH account/bank these transactions belong to. When more than one bank is connected, ALWAYS cross-check the echo against the account you intended to query and name the bank when presenting results — never attribute one bank's transactions to another. If total is 0 for a CREDIT account, check the connection health via openfinance_get_item_status — `statusDetail.creditCards.isUpdated: false` means the credit card sync failed and a force sync (openfinance_force_sync) or reconnection may be needed. May include a `provider_incident` block when the Open Finance provider has an OPEN incident affecting a connected bank: transactions may come back incomplete or wrong until the provider recovers, and reconnecting does not fix it. Bulk support: accepts account_ids for batched execution.
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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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  • Checks the LIVE operational status of the Open Finance provider (its public status page) — this is the PROVIDER's health, separate from your own connection's `openfinance_get_item_status`. Use it whenever data looks incomplete or stale even though a connection shows UPDATED (accounts/transactions/balances missing, a bank not returning everything): it reveals an upstream outage or a known incident on a specific bank/connector, so you can tell a provider-side problem apart from a connection that just needs reconnecting. Returns the global indicator (none/minor/major/critical), degraded components, open incidents, and — when you have banks connected — flags the incidents that affect YOUR connected banks in `your_banks_affected`.
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  • Lists loan contracts per bank connection (GET /loans). Pass `items` as an array of connection selectors (item_id uuid, connector_id, or connector_name) — one entry per connection to fetch; multiple connections are queried sequentially with rate-limit spacing. OMIT `items` to list loans across ALL linked banks. Returns `{ results, errors }` per connection.
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  • Returns bill-level detail for one or more credit card bills by id (GET /bills/:id): financeCharges and payments[] (id, paymentDate, amount, valueType, paymentMode). Does NOT return individual transactions — to get itemized credit card transactions (purchases, subscriptions, etc.), use openfinance_list_transactions with the credit card account_id and a from/to date range matching the bill's billing cycle (approximately dueDate − 30d to dueDate); each transaction MAY carry a `creditCardMetadata.billId` hint toward its bill, but it's sparse/inconsistent on some connectors (e.g. Nubank), so do NOT reconstruct a bill total by summing transactions by billId — the bill's own `totalAmount` is authoritative. Pass `bill_ids` as an array — use openfinance_list_credit_card_bills first to discover ids. `{ results, errors }` batch shape. NOTE: Pluggy does NOT return a paid/status field. In Brazilian Open Finance, `payments[]` reflects payments registered during THIS bill's billing cycle — typically the payment of the PREVIOUS bill (do NOT assume this bill was paid just because `payments[]` is non-empty). To check paid status, prefer `openfinance_list_credit_card_bills` which derives `payment_status` via cross-bill match.
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  • List all dimension type codes and human-readable titles available in the WHO Global Health Observatory API. Use this to discover valid dimension codes before calling who_list_dimension_values. Common dimensions include COUNTRY, REGION, SEX, WORLDBANKINCOMEGROUP, and AGEGROUP, but many additional types exist — this tool exposes them all.
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  • Search the WHO Global Health Observatory indicator catalog by keyword in the indicator name. Returns indicator codes and names for use with who_query_indicator_data. The search uses a substring match on indicator names — try terms like "life expectancy", "immunization", "mortality", "diabetes", or "HIV". If results are truncated, refine the query or use who_list_indicators to browse by offset.
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  • Fetch metadata for one or more WHO GHO indicator codes: the full indicator name and the dimensions it supports (e.g. COUNTRY, REGION, SEX, YEAR, WORLDBANKINCOMEGROUP, AGEGROUP). Call this before querying data with who_query_indicator_data to confirm which filter dimensions are valid for a given indicator. Accepts up to 10 codes per call. Codes with no metadata are reported in the notFound array rather than causing an error.
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,558 across 1461 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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  • 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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  • Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.
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  • Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
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  • Forces the bank to re-sync one or more connections NOW and WAITS for it to finish (PATCH /items/:id, then polls until the item stops updating, up to ~60s). Use this when a balance or transaction list looks stale: a connection can read UPDATED yet be hours old, and this pulls fresh data WITHOUT disconnecting/reconnecting. Pass `items` as an array of selectors (item_id, connector_id, connector_name, or the user-set custom_label nickname); OMIT `items` to sync ALL linked banks. Returns `{ results, errors }`; each result has the final `status`, `executionStatus`, `lastUpdatedAt` (advances when data is refreshed), and `synced` (true = fresh data is ready). `needs_action` (e.g. MFA_REINTERACTION / LOGIN_ERROR / WAITING_USER_INPUT) means the user must re-authenticate — those results include a `reconnect_url` that opens the widget in UPDATE mode for that exact connection (user enters credentials / MFA token, data refreshes in place, no slot consumed, no disconnect needed). `timed_out: true` means the sync is still running — re-check with openfinance_get_item_status. Set `wait: false` for fire-and-forget (returns immediately while UPDATING).
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