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457,902 tools. Updated 2026-08-14 18:25

"Warp" matching MCP tools:

  • Price MANY lanes in ONE call (parallel, ~1-3s for typical spreadsheets). Use this WHENEVER the user gives you a spreadsheet, CSV, or list of multiple lanes to quote — do NOT call warp_*_quote in a loop. Returns a single batch-quote card with one row per lane (origin → dest · mode · pallets · price · transit). Each priced lane keeps its quote_id and can be booked individually with book ("book row 3").
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  • Book MANY already-quoted lanes in ONE call (sequential, one card charge per row). Use this after batch_quote when the user says "book all of them" or "book rows 1, 3, 5" — do NOT call book in a loop. Each row needs a quote_id (the same one batch_quote returned for that row). Pickup/delivery default to the shared addresses at the top level so a single warehouse → many destinations only needs one address pair. Returns a progress card showing per-row Booked/Failed status with tracking numbers.
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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 1455 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 5,529 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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  • 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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  • 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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  • 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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Matching MCP Servers

  • A
    license
    A
    quality
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    maintenance
    MCP server for the Ringer WARP platform giving AI agents 135 tools to manage SIP trunking, phone numbers, porting, messaging, billing, and analytics.
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    MIT
  • A
    license
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    quality
    C
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    Quote, book, and track real LTL, FTL, cargo van, and box-truck freight through the Warp network - 20 tools, in-chat login, Stripe-charged bookings, and real carrier dispatch. Quoting is keyless; booking needs a free Warp account with a card on file.
    20
    395
    3
    MIT

Matching MCP Connectors

  • Quote, book, and track LTL, FTL, cargo van, and box-truck freight via the Warp API.

  • GW1 build compiler: skill data, template code encode/decode, validation, hero roster. Read-only.

  • Quote an LTL shipment — returns Warp's all-inclusive rate FAST (~1-2s) so the user sees a price immediately. The inline quote card shows the Warp rate plus a 'finding other carrier rates…' loading indicator. IMMEDIATELY follow up by calling ltl_market_options with the same parameters to fill in the multi-carrier comparison (~15s). Provide dims + commodity for an exact firm quote; if you don't have dims, quote anyway — it assumes a standard 48x40x48 pallet (FAK, no freight class) for an instant price. Don't block on asking for pallet dimensions; quote first, then pass real dims for an exact rate. When a palletized load could also move by box truck or van, quote LTL alongside those and show the cheapest valid mode. Do not editorialize the results. Do not declare a winner or recommend a specific carrier. Present Warp's quote first, then list market options as context. Let the user decide.
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  • Multi-carrier LTL comparison — returns 30+ carrier rates ranked by price (slow, ~15s). Call IMMEDIATELY AFTER ltl_quote with the same parameters; this fills in the 'finding other carrier rates…' section the fast quote card was showing. Useful when the user wants to compare carriers or pick a specific one. Do not declare a winner or recommend a specific carrier; just present the ranked list.
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  • Check or stop a recurring lane automation. Actions: 'status' (read-only), 'pause', 'cancel' (permanent), 'skip_next' (skip one week's pickup — nothing books or charges that week). STOP-ONLY by design: there is no agent-side resume, reactivate, or ceiling change — those exist only behind the account owner's emailed approval link. Auth required.
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  • Use a consumable item from cargo (Consumes an item for its effect. Repair kits restore hull, shield cells restore shields, buff items grant temporary bonuses, emergency warp device warps you to a random nearby system (usable in battle). Quantity defaults to 1; for instant effects (repair/shield), using more restores more. For buffs, only 1 is consumed (refreshes duration). Use 'refuel' command for fuel cells. Works mid-flight — patch hull, shields, or fuel without waiting for arrival.)
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  • Look up a GW1 hero by name or by id (GWCA HeroID, matching the AccountExport plugin output). Returns profession, campaign and how the hero is unlocked. Remember: heroes can equip any skill unlocked at ACCOUNT level, but NO PvE-only skill at all — including Signet of Capture. validate_build with forHero=true reports each one as an error, not a warning. Use this for one known hero; to browse or filter the roster, use list_heroes instead.
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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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  • "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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  • "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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  • 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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  • Fetch Chainstack's public pricing and return a normalized snapshot. Use this to answer pricing questions before quoting the user: plan fit, overage math, per-chain dedicated-node costs, and add-on pricing (Unlimited Node flat-fee tiers, Yellowstone gRPC streams, Warp transactions, dedicated-node base rates). This tool returns the menu, not the bill — the calling agent does the arithmetic. All prices are list prices in USD; disclaimers are surfaced in the `disclaimers` field. Design: we pass pricing.md through as raw markdown. Marketing owns that file and its structure changes freely; parsing it server-side would couple us to heading text and table column names we don't control. The LLM reads markdown natively, so handing the raw text to the agent keeps us correct regardless of how the page is restructured. pricing_current.json is parsed into `dedicated_catalog` because it has a stable engineering-owned schema, and the catalog benefits from filtering (to user-orderable SKUs only), unit conversion (cents → USD, milli-cores → cores), and region humanization (via `region_legend`). Per-method RU billing rules are NOT in these sources. Plan-level rates (Full Node = 1 RU, Archive Node = 2 RU) are in the markdown, but some EVM archive-state methods (eth_getBalance, eth_call, eth_getProof, eth_getStorageAt, eth_getCode, eth_getTransactionCount, eth_callMany, eth_createAccessList) and all debug_* / trace_* methods are billed at 2 RU on a full node when called against old blocks. For method-level detail, call `search_docs` with "request units" or `get_doc_page("docs/request-units")`. No API key required — sources are fully public. Each call fetches both sources fresh (no caching), so a stale result isn't possible. Returns: A dict with fields: - `pricing_markdown`: raw markdown from chainstack.com/pricing.md. Read this for plan tiers, feature matrix, add-on pricing, support levels, PAYG details, and provider comparisons. - `dedicated_catalog`: user-orderable per-chain dedicated-node SKUs with flavor, regions (as infra slugs like "sgp1"), hourly and monthly prices in USD. Already filtered to the ~87 orderable SKUs and unit-converted. - `region_legend`: slug → human city name map covering every region slug that appears in `dedicated_catalog`. Use `region_legend[slug]` to translate for display; `regions` keeps the slug as the canonical identifier. - `disclaimers`: list-price caveats (Enterprise "from" pricing, etc.). - `sources`: URL + ok/error per source; the JSON source carries its own `updated_at`. - `warnings`: populated when a source is unreachable or the JSON parser failed. The tool still returns best-effort results. - `fetched_at`: UTC timestamp of this call.
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  • Decode an in-game GW1 skill template code (e.g. "OwpiMypMBg1cxcBAMBdmtIKAA") into professions, attribute allocations and the 8 skills with their stats and descriptions. This decodes a SINGLE build code; for a multi-hero paw-ned2 team blob, use decode_pawned_team instead.
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  • Compile a build (professions, attributes, 8 skills by exact English name) into an official in-game template code. The build is validated first; on rule violations the errors are returned instead of a code. Unknown skill names return closest-match suggestions. IMPORTANT: template codes MUST come from this tool — never write or guess a code by hand, hand-written codes are invalid in-game. If unsure, verify any code with decode_template.
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,529 across 1455 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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  • "What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI when a ticker is implied; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, or company name as input), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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