"Finding Fishing Spots for the Weekend" matching MCP tools:

• deep_research

  Exoplanet Archive

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  nouel

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• nps_find_events

  national-parks-mcp-server

  Scheduled events at a park within a date range — ranger programs, festivals, tours, interpretive events — answering "what's happening at Yellowstone this weekend?" with title, dates and times, location, category, fee, and registration links. Get park codes from nps_find_parks. Paginates by page number, not offset. The events feed is sparser and less consistent than alerts or campgrounds; many parks list few or no events, and an empty result is not an error.

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• deep_research

  Twitch

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Hpo Api

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Crystallography

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Cms

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Linkedin_ads

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Microsoft Onenote

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Poetrydb

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• get_company_deadlines

  mcp

  Compute the upcoming Norwegian regulatory filing calendar for a specific organisation, looking horizon_months into the future. The response is one entry per (obligation, period) pair, each carrying: a stable obligation_id matching get_company_obligations; the due_date as an ISO 8601 timestamp in Europe/Oslo (DST-aware — the engine never hardcodes +01:00 / +02:00 and CET ↔ CEST transitions do not shift due dates by a calendar day); the legal_reference citation; a recurring boolean indicating whether the deadline repeats on a fixed cadence; and a business_day_adjusted boolean indicating whether the engine moved the date to the next Norwegian business day to land off a weekend or public holiday. Choose this tool when the agent needs the calendar view (when does the next MVA / A-melding / Årsregnskap filing land?) rather than the obligation menu. Pair with get_company_obligations to learn what each obligation_id requires. Inputs: { org_number } as 9 digits passing the Brønnøysund MOD-11 control-digit check, plus an optional horizon_months between 1 and 60 (defaults to the endpoint's standard horizon — matches the route's own horizonMonthsSchema clamp). Determinism (Rule 9): same input + same rulebook_version produces a byte-identical calendar. Failure modes: 404 NOT_FOUND for unknown org_numbers; SCOPE_INSUFFICIENT if the API key is not scoped read:brreg; VALIDATION_FAILED on shape, mod-11, or out-of-range horizon (boundary [1, 60] matching the underlying route — round-7 polish widened from [1, 24] so the tool no longer refuses horizons the REST surface accepts). Required scope: `read:brreg` (matches the underlying /v1/company/{org}/deadlines route's `SCOPE_REQUIREMENTS` binding — round-7 polish corrected an earlier `read:rulebook` declaration that would have produced SCOPE_INSUFFICIENT at runtime since the route checks read:brreg). For the per-obligation compliance verdict rather than the date calendar, use get_company_obligations instead.

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• deep_research

  Wayback

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Worldbank Poverty

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Emdb

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• pricetik_compare_destinations

  mcp-server

  Apples-to-apples destination comparison. Pass 2–4 destinations + same date window + same guest count. Returns median 4★ nightly rate, top-3 hotels per destination by deal score, top-3 activities, and a seasonal-premium flag (event-window markers like "F1 weekend in Vegas"). Use `pricetik_destinations` first to resolve activity codes if you do not already have them (its `activityDestinationCode` field — the short Hotelbeds CODE like "LAS", not the destination UUID). Live-event tickets are not compared here — after picking a destination, bundle them via pricetik_trip_plan includeTickets=true or pricetik_ticket_search. Stricter rate limit than other tools (10/min) — fans out 2–4 parallel hotel + activity searches.

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• deep_research

  Overheid Nl

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Datagov Uk

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• find_thinkers_like

  Rodin

  Given a passage of text (essay, note, message, snippet, transcript), returns ~5 humans whose intellectual fingerprint matches it — recurring themes, mental models, archetypal stance, blind spots. Use when the principal asks for sparring partners, intellectual peers, "who else is wrestling with this," "who thinks like X," or "find me writers similar to this passage." Each result returns a name, three-word archetype, one-line summary, dominant themes, and a profile URL the principal can visit. The match runs over Voyage 3.5-lite text embeddings reranked by a proprietary 12-dimensional cognitive-style vector — so results align by *how* a mind reasons, not just topical overlap.

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• deep_research

  Ilostat

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• deep_research

  Dbnomics

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,775 tools across 895 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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