"A financial data and news platform called Tiingo" matching MCP tools:

• get_public_info_api_infoA

  orderly-mcpA

  Get reference documentation for the Orderly Public Info API — a zero-auth single endpoint (POST /v1/public/query) for market, account, and platform data selected by a `type` field. Covers 24 query types (marketSummary, accountState, orderbook, candles, topAddresses, whaleContext, etc.); `queryType` accepts exact, partial, or fuzzy names and returns params, response fields, example, weight, and freshness; `category` lists a group (queryType wins if both are supplied); omit both for the overview and rate-limit rules. Read-only bundled docs — it does not call the endpoint. Use instead of get_api_info for zero-auth public data; use get_api_info for authenticated trading, get_indexer_api_info for historical analytics, and get_strategy_vault_api_info for vault data.

  ConnectorNo auth

• list_onchain_indicatorsA

  gavelA

  This tool has moved. Cross-venue credit data is served by the Bitcoin Credit Stack MCP at https://mcp.bitcoincreditstack.com/mcp, where this tool is called `list_onchain_indicators`. Calling it here returns a structured 'moved' error naming the host. This shim is temporary and will be removed.

  ConnectorNo auth

• get_mvrvA

  gavelA

  This tool has moved. Cross-venue credit data is served by the Bitcoin Credit Stack MCP at https://mcp.bitcoincreditstack.com/mcp, where this tool is called `get_mvrv`. Calling it here returns a structured 'moved' error naming the host. This shim is temporary and will be removed.

  ConnectorNo auth

• latest_newsA

  NewsdataA

  Get news headlines and articles — world news, breaking news, and business/financial/stock-market news. Filter by keyword, country (2-letter, e.g. "us"), category (business, technology, politics, sports, health, science), and language. IMPORTANT: for stock-market / financial-market / economy / "world market news" questions, ALWAYS pass category: "business" — it returns real market-news outlets and filters out low-quality SEO/crypto-promo articles. Returns article title, description, link, source, publish date, category, and country. Paginate via the nextPage token. WITHOUT from_date/to_date this covers only roughly the LAST 48 HOURS (NewsData.io's rolling "latest" feed) — asking about an older event with no date window silently returns today's unrelated articles instead of an empty result. For a PAST date range, pass from_date/to_date (YYYY-MM-DD): this routes to NewsData.io's archive endpoint, which requires a plan tier above the shared platform key's default — if the account cannot serve it, the call returns a `filter_gap` field naming the unsupported window instead of silently substituting the latest 48h. Examples: latest_news({ query: "stock market", category: "business" }) for current world market news; latest_news({ query: "election", country: "us", category: "politics" }); latest_news({ query: "Houthi Yemen attack", from_date: "2026-08-01", to_date: "2026-08-15" }) for an August event.

  ConnectorNo auth

• get_account_metricsA

  SocialGPTA

  Get per-platform engagement (views / likes / comments / shares) as a time series over the trailing window_days (default 28, up to 365). Omit account_id to aggregate across all connected accounts, or pass one from list_accounts; optionally filter to a single platform. post_limit (≤100) fixes how many recent posts form the baseline. granularity buckets the series server-side ('daily' default, 'weekly', or 'raw' for every scrape). Read `series` (a clean per-platform list of typed points) — `metrics` is the legacy column/data matrix kept for back-compat. NB: follower counts here are latest-only; for audience growth over time use get_follower_history.

  ConnectorOAuth

• search_creatorsA

  InflushipA

  Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.

  ConnectorNo auth

• search_creatorsA

  Influship Influencer Marketing MCPA

  Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.

  ConnectorNo auth

• list_connectorsA

  AutarioA

  List the platform connectors set up on this Autario account: search, web and product analytics, ads, social, commerce, payments, AI usage and developer platforms. The answer names each connected platform, so call this instead of guessing which ones exist. Each entry carries its live dataset_id (queryable via query_dataset), datasets[] (ALL datasets the connector materialized | multi-report connectors produce one per report), refresh interval, last refresh time, and `health`: state (ok | stale | needs_sign_in | reports_missing | retrying | ours | never_ran | running), how old the data is per dataset, and next_step { who: assistant | user | nobody, action, url? }. CHECK health.state BEFORE answering from a connector table: ok = use it; stale = call refresh_connector once; needs_sign_in = give the user the one sign-in link in next_step.url, do not refresh; reports_missing = say which reports and why (get_connector_status has the per-report sentences); retrying or ours = answer from the data you have and say how old it is. Use this whenever a user asks about "my Search Console data", "my ad spend", "my store orders" or wants a cross-platform weekly report. Connectors are created and configured by the account owner in the Autario UI (autario.com/manage) | this tool only reads them, it never changes settings or handles credentials. Requires AUTARIO_API_KEY.

  ConnectorNo auth

• publish_templateA

  KamyA

  Make a template version live for rendering. Called with no `version`, it snapshots the current draft into a new version and points published_version at it — this is how you ship an edit made with update_template. Called with an existing `version`, it republishes that earlier snapshot and leaves the draft alone. Reach for rollback_template instead when you are reverting a bad release: only that tool offers the concurrency fence and the option to restore the draft as well. Takes a template UUID or slug, same as every other /v1/templates route. Returns { templateId, publishedVersion, publishedVersionId, latestVersion, publishedAt }. Requires a Kamy API key with the `templates:write` scope; without a key, returns dashboard setup instructions.

  ConnectorNo auth

• publish_templateA

  KamyA

  Make a template version live for rendering. Called with no `version`, it snapshots the current draft into a new version and points published_version at it — this is how you ship an edit made with update_template. Called with an existing `version`, it republishes that earlier snapshot and leaves the draft alone. Reach for rollback_template instead when you are reverting a bad release: only that tool offers the concurrency fence and the option to restore the draft as well. Takes a template UUID or slug, same as every other /v1/templates route. Returns { templateId, publishedVersion, publishedVersionId, latestVersion, publishedAt }. Requires a Kamy API key with the `templates:write` scope; without a key, returns dashboard setup instructions.

  ConnectorNo auth

• dropyour_linkA

  DropyourA

  Let one of your graduated apps call ANOTHER of your apps over the network. By default no app can reach another — that refusal is the platform's isolation, not an oversight. This opens exactly the app you name, by its dropId, and only if it belongs to the SAME account: a link never crosses two accounts. action='delier' revokes it (and its contract), and revoking asks nothing — taking a right back cannot be harder than granting it. Eight links at most: beyond that it is not a contract between apps any more, it is a network, and it gets decided differently. Over a link, the called app knows who calls: its server code sees app.caller = { dropId } of the calling app, signed by the platform and impossible to forge; every other request sees null. CONTRACT (optional, recommended): pass `contrat` with action='lier' to state what the calling app relies on — up to 8 READ routes (GET or HEAD), each with the status it expects (default 200) and, for GET, a MINIMAL JSON Schema of the response (extra fields are always allowed). From then on, every new version of the called app published with dropyour_release, dropyour_replace or dropyour_patch is checked against it BEFORE it stays live: if a route breaks, the version is refused, the previous one is put back, and the answer names the consumer and the route. Calling 'lier' again with a new `contrat` replaces it (its revision goes up); action='retirer_contrat' removes it and keeps the link. Write methods are refused in a contract: the checks run against the called app's REAL data. action='consommateurs' (dropId = the CALLED app, no cible) lists which apps rely on it and under which contract. What a link does NOT do: it grants no token and no shared storage. Your app calls the other one over plain HTTP, and the other one's own guards apply unchanged: it decides what a linked caller may do.

  ConnectorNo auth

• everyinfra_list_capabilitiesA

  EveryInfraA

  List EveryInfra's data platforms and capabilities (106 platforms / 573 capabilities: search, detail, comments, profiles and more). Use it before everyinfra_call_api to get the platform/action, required and optional params, the per-call row limit (max_limit, or page_size for page-based actions) and the price. Pass a platform to also get allowed values, returned fields and what they mean. Free. Data collection only — for AI chat use everyinfra_chat.

  ConnectorNo auth

• deep_researchA

  Ai FeedsA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  Nbu UaA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  Georgia ArchivesA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  Data BostonA

  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 1863 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 7,176 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).

  ConnectorNo auth

• get_best_timesA

  OmniSocialsA

  Get recommended posting times (day + hour) for one platform, computed from the workspace's own posting history: publish time × engagement of every post published there, recency-weighted and outlier-damped, bucketed in the user's timezone, and blended with when the account's followers are online when the platform provides that (Instagram, TikTok Business; `basis: own_data_and_audience`, response carries `audience_online`). Returns the top 3 recommended slots plus a per-day breakdown. When the workspace has fewer than 15 analyzed posts on the platform, the audience-online profile alone is used (`basis: audience`) or industry-average defaults are returned (`basis: defaults`) with how many more posts unlock personalized recommendations — tell the user that so the numbers aren't mistaken for their own audience data. Use this before scheduling when the user asks 'when should I post?' or hasn't specified a time. Requires the analytics:read scope.

  ConnectorOAuth

• deep_researchA

  Nplg CatalogA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  KartaviewA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  SmartyA

  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 1863 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 7,176 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).

  ConnectorNo auth

• deep_researchA

  Arcgis SumterA

  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 1863 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 7,176 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).

  ConnectorNo auth