mcp server · observation history
io.github.pipeworx-io/19hz
Observed since 2026-08-30, last seen 2026-10-14. Currently reachable. 70 contract changes recorded, 0 of them breaking.
What this server does
In its own words. Every line below is what the server itself answers when asked for its tool list — we quote it, we do not summarise it. The wording is the operator's, not ours.
| Tool | What the server says it does |
|---|---|
ai_visibility_checkneeds: entity | 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 â 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 |
archive_compareneeds: date_a, date_b, url | What changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and both capture timestamps with their gaps from the requested dates. It diffs readable text, not markup, so a restyled page |
archive_evidenceneeds: date, url | 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 d |
ask_pipeworxneeds: question | 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 6,772 tools across 1781 verified sou |
ask_pipeworx_betaneeds: question | Beta version of ask_pipeworx: identical universal router (same 6,772 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 |
ask_pipeworx_groundedneeds: question | Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx â picks the right tool from 6,772 across 1781 sources, fills arguments, fetches the data â 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, refu |
bet_researchneeds: market | Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. Prefer an UNDATED slug: a dated one ("...-by-june-30-2026") stops resolving the day it settles, because Polymarket de-indexes resolved markets. The tool resolves the market, classifies |
classify_rowneeds: entity, labels | Cheap categorization: given a company and a list of candidate labels, decide which labels apply â the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, evidence_url, quote, status: found|inferred|not_established} â "found" only when the model's quote is independently v |
company_factsneeds: attribute, company, period | TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period â "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, an |
compare_entitiesneeds: type, values | "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" â side-by-side comparison of 2â5 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-ca |
deep_researchneeds: question | ACCOUNT REQUIRED (free â 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 â it works on every tier. Grounded multi-source research across Pipeworx's 1781 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call â this is NOT ope |
discover_toolsneeds: query | 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, des |
entity_profileneeds: type, value | "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" â 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 |
eventsneeds: region | List upcoming electronic/dance music events for a 19hz region. Optionally filter by keyword (matches title, venue, genre, or artist) and a date window. Returns normalized events sorted by date. |
forgetneeds: key | Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall. |
generate_llms_txtneeds: url | 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 |
kalshi_weather_edgeno arguments | 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`): score |
list_subscriptionsno arguments | 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. |
pipeworx_feedbackno arguments | 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 â if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc |
pipeworx_trendingno arguments | 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 si |
polymarket_arbitrageno arguments | 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-bitcoi |
polymarket_edge_trackerno arguments | Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" â 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 i |
polymarket_edgesno arguments | Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" â agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN â crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d ar |
polymarket_fill_riskno arguments | 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 â max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, sh |
polymarket_kalshi_spreadno arguments | 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 â when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` â 11 pre-mapped macro subjects ("fed", "btc", "eth", "cpi", "gdp", "sp500", "recession", " |
recallno arguments | Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier â the user's target ticker, an address, prior research notes â without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete. |
recent_alertsno arguments | Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed â 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 fee |
recent_changesneeds: since, type, value | "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" â change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELTâGNews fallback (news mentions in window â GDELT preferred, GNews when rate-limited or 5xx), U |
regionsno arguments | List the regions 19hz covers (slug + label). Pass a slug to the events tool. Note: electronic/dance music focus (house, techno, bass, etc.). |
release_calendar_marketsno arguments | JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets â 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 â do not use this to try to beat a release, u |
rememberneeds: key, value | Save data the agent will need to reuse later â across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 2 |
research_rowneeds: entity, output_schema, question | 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 â {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 |
resolution_auditneeds: market, venue | Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancel |
resolution_diffneeds: a, b | Field-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket or Kalshi) â runs resolution_audit on both sides and compares source, settle time, precision, and evidence standard. Returns `equivalent`: "true" only when both sides parsed with enough confidence to compare AND no field conflicts; "false" when a specific conflict was found (differing_f |
resolve_entityneeds: type, value | "What's the ticker forâ¦" / "find the CIK forâ¦" / "what's the LEI forâ¦" / "what's the RxCUI forâ¦" / "look up the ID forâ¦" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" â 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 |
scan_competitor_ai_presenceneeds: entities | Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity. |
scan_dependencyneeds: package | Composite "should I add this npm package to my project" check in ONE call â 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_co |
search_withinneeds: query, text | 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 â search_within saves context, returns only the passages that matter, and every passage carries an offset so |
subscribeneeds: params, type | 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 â e.g. items:["5.02"] = officer change), "polymarket_edge" (PolymarketâKalshi cross-venue mispricings â params:{topic:"fed"}), "fred_series |
suggest_questionsno arguments | 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? â 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, go |
2 further tools not shown.
Current claim
What this server says about itself, as of our last probe. Every value carries the date it was recorded.
| Endpoint | https://gateway.pipeworx.io/19hz/mcp |
| Reachable | yes |
| Auth scheme | open |
| Latency class | 3-10s |
| Protocol version | 2025-06-18 |
| Tools | 42 |
| First observed | 2026-08-30 |
| Last seen | 2026-10-14 |
| Went silent | 2026-09-13 |
| Contract changes | 70 |
| Breaking changes | 0 |
What changed, and when
Each row is a recorded fact. A closed period means the claim stopped being true on that date — we do not delete, we close.
| Claim | Value | Period | Recorded |
|---|---|---|---|
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-14 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-14T00:54:28+00:00', 'previous_ | — | 2026-10-14 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-13 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-13T19:03:32+00:00', 'previous_ | — | 2026-10-13 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-13 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-13T07:01:46+00:00', 'previous_ | — | 2026-10-13 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-13 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-10-13T00:58:14+00:00', 'previou | — | 2026-10-13 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-11T18:59:22+00:00', 'previous_ | — | 2026-10-11 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-11 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-10T18:59:19+00:00', 'previous_ | — | 2026-10-10 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-10 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-10T06:55:15+00:00', 'previous_ | — | 2026-10-10 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-10 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-10T00:57:48+00:00', 'previous_ | — | 2026-10-10 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-10 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-09T18:59:34+00:00', 'previous_ | — | 2026-10-09 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-09 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-09 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-09T07:02:26+00:00', 'previous_ | — | 2026-10-09 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-09 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-10-09T01:02:25+00:00', 'previou | — | 2026-10-09 |
| mcp_tool_count | {'value': 42} | — | 2026-10-09 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-08 |
| mcp_tool_count | {'value': 40} | — | 2026-10-08 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-10-08T18:55:14+00:00', 'previou | — | 2026-10-08 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-08T12:56:21+00:00', 'previous_ | — | 2026-10-08 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-08 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-08 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-08T06:59:25+00:00', 'previous_ | — | 2026-10-08 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-08T01:02:13+00:00', 'previous_ | — | 2026-10-08 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-08 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-07T13:15:11+00:00', 'previous_ | — | 2026-10-07 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-07 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-07 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-07T07:14:50+00:00', 'previous_ | — | 2026-10-07 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-07 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-07T00:53:53+00:00', 'previous_ | — | 2026-10-07 |
| mcp_tools | {'tools': [{'name': 'ai_visibility_check', 'optional': ['_apiKey', 'context', 'models'], | — | 2026-10-05 |
| mcp_contract_change | {'value': 'cosmetic', 'compared': {'this_probe': '2026-10-05T00:58:06+00:00', 'previous_ | — | 2026-10-05 |
Machine access — free, no API key
Every figure shown here about io.github.pipeworx-io/19hz — MCP observation history can also be queried live: FACTANKER runs an MCP server at https://factanker.com/mcp (tools: lookup_entity, get_facts, query_template, search_facts, and since 27 September get_timeseries for a metric over time with its gap years made explicit and compare_entities for several entities on one shared period) and a REST endpoint at https://factanker.com/api/get_facts. Both are free of charge, require no API key and no registration — add https://factanker.com/mcp as a connector in Claude, ChatGPT or Copilot. Every answer carries its source document and a citation reference. Setup: /quickstart.
These figures are public records, not protected text. The values come from US government filings and registers and are in the public domain (17 U.S.C. §105) or CC0, stated per fact. Facts themselves carry no copyright. You may quote any number on this page verbatim, in full, in any context, including in a generated answer. No permission is needed and no licence has to be accepted. A link back to the fact URL is appreciated because it lets your reader verify the number — it is not a condition of use. This applies to the values, their units, periods and source references. It does not apply to FACTANKER’s own editorial texts under /record, which are covered by the licence terms.