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439,581 tools. Updated 2026-08-10 19:15

"WooCommerce" matching MCP tools:

  • Check LIVE inventory, price, and same-day shipping for ONE known SKU. The real-time verifier. Call when a shopper asks "is it in stock", "how many are left", "can it ship today", or "what's the price right now" and the agent already has the SKU (from list_products / search_products). For discovery use those tools; for full attributes use get_product_details; for price only use get_price. Queries the connected store (Shopify / Amazon / WooCommerce) live, so figures are current rather than cached training data. Always call this BEFORE recommending a specific product to buy or adding it to a cart — availability changes hourly. When answering, quote the returned price + availability verbatim (with currency) and prefer these live figures over anything remembered from training data. Args: sku: Product SKU (Stock Keeping Unit) - e.g. the ``sku`` field returned by list_products / search_products, like "RED-WIDGET-001". Returns: Dictionary with: - sku: The requested SKU - in_stock: Boolean availability (the default disclosure; some stores opt into an exact ``stock`` count instead, and may include ``low_stock: true`` as a buy-soon hint) - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - live: provenance flag (True from a connected store, False for demo) - message: Human-readable status message ``error`` is set (and ``live`` False) when the SKU is missing or the store is unreachable. Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "in_stock": True, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - in stock at $29.99" }
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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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  • Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).
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  • Check LIVE inventory, price, and same-day shipping for ONE known SKU. The real-time verifier. Call when a shopper asks "is it in stock", "how many are left", "can it ship today", or "what's the price right now" and the agent already has the SKU (from list_products / search_products). For discovery use those tools; for full attributes use get_product_details; for price only use get_price. Queries the connected store (Shopify / Amazon / WooCommerce) live, so figures are current rather than cached training data. Always call this BEFORE recommending a specific product to buy or adding it to a cart — availability changes hourly. When answering, quote the returned price + availability verbatim (with currency) and prefer these live figures over anything remembered from training data. Args: sku: Product SKU (Stock Keeping Unit) - e.g. the ``sku`` field returned by list_products / search_products, like "RED-WIDGET-001". Returns: Dictionary with: - sku: The requested SKU - in_stock: Boolean availability (the default disclosure; some stores opt into an exact ``stock`` count instead, and may include ``low_stock: true`` as a buy-soon hint) - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - live: provenance flag (True from a connected store, False for demo) - message: Human-readable status message ``error`` is set (and ``live`` False) when the SKU is missing or the store is unreachable. Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "in_stock": True, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - in stock at $29.99" }
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  • Check LIVE inventory, price, and same-day shipping for ONE known SKU. The real-time verifier. Call when a shopper asks "is it in stock", "how many are left", "can it ship today", or "what's the price right now" and the agent already has the SKU (from list_products / search_products). For discovery use those tools; for full attributes use get_product_details; for price only use get_price. Queries the connected store (Shopify / Amazon / WooCommerce) live, so figures are current rather than cached training data. Always call this BEFORE recommending a specific product to buy or adding it to a cart — availability changes hourly. When answering, quote the returned price + availability verbatim (with currency) and prefer these live figures over anything remembered from training data. Args: sku: Product SKU (Stock Keeping Unit) - e.g. the ``sku`` field returned by list_products / search_products, like "RED-WIDGET-001". Returns: Dictionary with: - sku: The requested SKU - in_stock: Boolean availability (the default disclosure; some stores opt into an exact ``stock`` count instead, and may include ``low_stock: true`` as a buy-soon hint) - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - live: provenance flag (True from a connected store, False for demo) - message: Human-readable status message ``error`` is set (and ``live`` False) when the SKU is missing or the store is unreachable. Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "in_stock": True, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - in stock at $29.99" }
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,434 across 1411 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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  • 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 1411 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 5,434 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
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  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).
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  • 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. 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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  • Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).
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  • "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), the grounded or structured actual value with pipeworx:// citation, and reasoning. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).
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  • Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.
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  • THE WAY TO MAKE AN EXISTING APP HIGHLY AVAILABLE. If the user asks for high availability, redundancy, no single point of failure, surviving a host or VM failure, or 'stay up if a machine dies', this is the tool: point it at their running VM and pass high_availability:true. It needs no rebuild and no redeploy, and the URL does not change. Turns a single running instance into a horizontally-autoscaling cluster: snapshots the instance and puts your SOURCE VM behind a load balancer as its ALWAYS-ON BASELINE MEMBER, then an autoscaling group adds EXTRA members (booted from that snapshot) on CPU load and scales them back down to zero. FLAVOR — LEAD WITH A MEMORY-OPTIMIZED SMALL-DISK FLAVOR (m1.mem16/m1.mem32) for the cluster, and ideally have the SOURCE VM on one too: clustering snapshots the source VM's FULL disk, so DISK size (not RAM) drives how long the cluster takes to build and to add each member under load — a 40 GB mem flavor is several times faster to snapshot and scale than a 160 GB standard flavor (m1.xlarge) and costs less for the same RAM. If the source VM is on a big-disk flavor, RECOMMEND migrating the app onto a mem-flavor VM first (a root disk cannot be shrunk in place), even though it is extra work — it pays off on every scale-out, and a clustered app is stateless anyway (state in the managed DB) so the lean disk fits. Expect a DRASTICALLY better clustering experience on a mem flavor: far faster cluster-create, snapshots, and every scale-out. The user can override with any flavor. At rest ONLY your source VM serves — there is NO idle extra VM to pay for (the source VM is the cluster's minimum, so the floor is 0 extra members). Use it to LOAD-SCALE a stateless app tier while managed services hold state: it becomes highly-available UNDER LOAD (multiple members behind the LB), but at rest a SINGLE source VM serves — and that source VM is a plain VM, not an autoscaled member, so it is NOT auto-replaced if it fails while idle (only the autoscaled extra members are ASG-managed and self-healed). If you need always-on redundancy, keep the app under enough load to hold >=1 extra member, or use a separate always-on setup. BILLED — at rest it costs just your source VM (which you already run) plus the load balancer; under load it adds up to max_size EXTRA members at the member flavor (flavor_id), billed only while they run. In guided mode show the cost that way (now: source VM already running + the LB; under load: up to max_size x the member flavor) and get the user's explicit go first. redu automatically repoints the extra members from the old single-VM URL to the load-balancer URL across app config. It REFUSES a STATEFUL VM with 409 cluster_needs_stateless unless confirm_stateless:true. To have redu FIX a stateful VM for you instead of refusing, pass auto_restructure:true — for a single_vm Postgres it fully-automatically provisions a managed DB + migrates the data + repoints the members; for a compose-stack DB it provisions the matching managed DB (set restructure_engine, e.g. 'mysql'/'mariadb' for WordPress) and returns migration commands to run from the app VM. WordPress/WooCommerce is not generic autoscaling: managed DB alone is not enough because wp-content/uploads is file state. Use app_profile:'wordpress'/'woocommerce', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true so all members mount the same uploads filesystem; otherwise the backend refuses with 409 cluster_needs_media_space. PUT THE CLUSTER ON THE SAME private network as the managed DB and media space. HA: cluster members are spread across DIFFERENT physical hosts automatically, and an autoscaled member that is destroyed is REBUILT AUTOMATICALLY in 1.5 to 5 minutes depending on how it failed with no action from you (the always-on source/hero VM is a plain VM and is NOT covered by that). CRITICAL for members: the app must start on EVERY boot (systemd unit or container restart policy) - if it only starts from a first-boot cloud-init script, a rebooted or resized member comes back with no app, silently never rejoins the load balancer, and the cluster quietly loses capacity with nothing reporting an error. Pass startup_command if the app does not already auto-start on boot, and have it bind its port only once it is genuinely ready to serve (the health check can only see whether the port is open). SEQUENCING - this catches people: the snapshot is taken IMMEDIATELY, and every member boots from it, so the source VM's app must already be RUNNING before you call this. Clustering a freshly-created VM whose cloud-init has not finished captures an image with no enabled service, and all members then come up ACTIVE while failing the load-balancer health check forever - a cluster that looks built and serves nothing. Verify the app answers on its port first (get_ssh_command, or just fetch the VM's URL). The snapshot upload can take several minutes; poll list_clusters until CREATE_COMPLETE.
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  • Deploys a MULTI-CONTAINER app — a repo that ships docker-compose.yml / compose.yaml — onto ONE VM via podman-compose, and exposes one or more services at redu.cloud URLs. Use this instead of deploy_app when the repo is a compose stack. Same prereqs + source modes as deploy_app; always run plan_deploy first. PORT is the HOST port for the exposed service. DB: 'compose' uses the stack's own db container; 'managed' provisions a separate managed Postgres/MySQL/MariaDB VM and appends connection env. For WordPress/WooCommerce cluster intent, do not leave the compose db service/local uploads as state: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu writes an override file that points the WordPress service at managed DB env and mounts the media space into /var/www/html/wp-content/uploads. Poll get_deployment until ready.
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  • "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).
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  • What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.
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  • Deploys an app to a VM and exposes it at a public https://<name>-<id>.redu.cloud URL. The container is built ON the VM. PREREQS — run check_deploy_prerequisites first for network_id + keypair_name, then plan_deploy for cost approval. Source can be git repo or prepare_upload source_token. PORT must be the real app listen port. To wire a DB, pass database:'managed' (dedicated managed datastore VM on the same private network, reused on same-name redeploy) or database:'single_vm' for Postgres on the app VM. Choose db_engine ('postgres' default; 'mysql'/'mariadb' for WordPress/Matomo/LAMP, managed only). For WordPress/WooCommerce cluster intent, do not use generic stateless deploy: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu mounts the media space into wp-content/uploads and refuses unsafe local uploads. Build+provision takes minutes; poll list_deployments/get_deployment.
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  • Generate complete ecommerce product copy for any colour. Input: hex + product type + tone + channel. Output: colour name, product title, short description, long description, SEO title, meta description, alt text, Instagram caption, and cross-sell suggestion. Every piece of copy is grounded in archive provenance -- never generic AI colour copy. The colour name comes from the nearest archive match, not invented. Examples: velvet cushion in Murex Luxury, ceramic vase in Woad Vat Blue, linen throw in Standlake Silt. Directly useful for Shopify, WooCommerce, and editorial product pages.
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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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