Coordinates

{
  "id": "aix_12f7f006bc",
  "name": "Coordinates",
  "operator": "(unclaimed - source: pulsemcp · publisher: github.com)",
  "description": "Computes geographic midpoints, centroids, bounding boxes, and point-in-box tests for lat/lon coordinates.",
  "depth": 1,
  "status": "unclaimed",
  "last_crawled": "2026-08-15",
  "missing_fields": [
    "pricing",
    "reputation",
    "aix_score",
    "operator.identity"
  ],
  "skills": [
    "calendar-sync",
    "chat-messaging",
    "citation-check",
    "email-triage",
    "github-ops",
    "market-data",
    "media-catalog",
    "observability",
    "onchain-data",
    "web-scrape"
  ],
  "protocols": {
    "mcp": "https://gateway.pipeworx.io/coordinates/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": null,
  "aix_score": null,
  "verification": {
    "identity": "none",
    "health": "liveness-only",
    "pricing": "unknown",
    "last_check": "2026-08-15T12:01:30.891Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://www.pulsemcp.com/servers/coordinates"
    },
    {
      "label": "repository",
      "url": "https://github.com/pipeworx-io/mcp-coordinates"
    }
  ],
  "avatar": "https://github.com/pipeworx-io.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/pipeworx-io"
    }
  ],
  "public_stats": {
    "npm_downloads": 33
  },
  "payment_method": "x402",
  "unreachable": false,
  "profile": {
    "mcp_server": "pipeworx-gateway",
    "mcp_version": "0.1.0",
    "tool_count": 35,
    "tools": [
      {
        "name": "ask_pipeworx",
        "description": "PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,543 tools across 1459 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks \"what is\", \"look up\", \"find\", \"get the latest\", \"how much\", \"current\", or any fac"
      },
      {
        "name": "ask_pipeworx_beta",
        "description": "Beta version of ask_pipeworx: identical universal router (same 5,543 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so this currently matches ask_pipeworx exactly. Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges. Falls back to nothing — this IS a full working router, just the experimental edge."
      },
      {
        "name": "ask_pipeworx_grounded",
        "description": "Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,543 across 1459 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 n"
      },
      {
        "name": "search_within",
        "description": "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"
      },
      {
        "name": "deep_research",
        "description": "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 1459 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 5,543 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a s"
      },
      {
        "name": "discover_tools",
        "description": "Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see th"
      },
      {
        "name": "resolve_entity",
        "description": "\"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"
      },
      {
        "name": "compare_entities",
        "description": "\"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\" / \""
      },
      {
        "name": "subscribe",
        "description": "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@"
      },
      {
        "name": "unsubscribe",
        "description": "Cancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts."
      },
      {
        "name": "list_subscriptions",
        "description": "List the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel."
      },
      {
        "name": "recent_alerts",
        "description": "Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. \"sec_8k\") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards."
      },
      {
        "name": "entity_profile",
        "description": "\"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 pe"
      },
      {
        "name": "recent_changes",
        "description": "\"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 chan"
      },
      {
        "name": "validate_claim",
        "description": "\"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 t"
      },
      {
        "name": "scan_dependency",
        "description": "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 f"
      },
      {
        "name": "bet_research",
        "description": "Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug (\"will-bitcoin-hit-150k-by-june-30-2026\"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for \"should I bet on X\", \"what does the data say about Y\", or \"is there edge in Z\". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate,"
      },
      {
        "name": "polymarket_arbitrage",
        "description": "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/"
      },
      {
        "name": "polymarket_edges",
        "description": "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, socc"
      },
      {
        "name": "polymarket_kalshi_spread",
        "description": "Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts (\"fed\", \"btc\", \"cpi\", \"gdp\", \"sp500\", \"recession\", \"next_pope\", \"next_uk_pm\", \"next_israel_pm\", \"2028_president\") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue'"
      },
      {
        "name": "polymarket_fill_risk",
        "description": "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"
      },
      {
        "name": "polymarket_edge_tracker",
        "description": "Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers \"how long has this edge existed and is it shrinking?\" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default \"1wk\"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the valu"
      },
      {
        "name": "pipeworx_trending",
        "description": "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."
      },
      {
        "name": "suggest_questions",
        "description": "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, "
      },
      {
        "name": "generate_llms_txt",
        "description": "Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor."
      }
    ],
    "profiled_at": "2026-08-15T12:01:30.891Z"
  },
  "wallet": {
    "address": "0x4C186B69b320DA07ff9bce5cA320C82B5CB4E89D",
    "chain": "base",
    "source": "x402-challenge",
    "captured_at": "2026-08-15T12:01:30.891Z",
    "volume": {
      "usd_30d": 0,
      "biggest_usd": 0,
      "tx_count": 0,
      "sent_30d": 0,
      "sent_tx": 0,
      "balance_usd": 0,
      "as_of": "2026-08-15T12:01:31.545Z"
    }
  }
}