aquaview-mcp

Quality 76 / 100good

6 tools, about 4,252 tokens to load them all · tool set 5eb2aae461ae · tools last changed 2026-10-16 · checked 2026-10-20 23:27 UTC

Tool changes

• 2026-10-16get_datachangeddescription
  before

  Pull actual observation rows from an AQUAVIEW data source — not catalog metadata. Use this whenever the question needs the measurements themselves (temperature profiles, float positions, buoy readings), rather than which datasets exist. Call list_data_sources first for a source's canonical variable names, its `qc_modes`, and its `engine` (gridded "xarray" sources have no rows to pull). READ 'returned' FIRST. It is one of three values: - 'complete': 'rows' holds EVERY matching row (or your whole 'limit'). Safe to compute anything from. - 'head': 'rows' holds the FIRST rows in the source's natural order (see 'head.order'), out of 'head.of' total. These are not a random sample — later rows differ systematically — so never average or take a maximum from them. To get more, set 'limit' up to 'head.max_inline'; beyond that, narrow the request or fetch 'download_url'. - 'none': no rows. 'refusal.reason' says why. 'unscoped_query' means add a bbox/datetime/depth/filter. 'no_natural_order' means the source declares no ordering, so no partial set can be given honestly — narrow until it fits, or use 'stats'. The rest are transient; retry. OTHER FIELDS: - 'stats': exact minimum, maximum, mean and spread computed over EVERY matching row. Use these for any numeric claim — exact at any data size. - 'stats_basis': what shaped those numbers. If 'qc' is null the values are UNSCREENED and may include implausible outliers from bad sensor readings; say so, or re-run with a quality filter. - 'structure': how many distinct floats/stations/profiles matched — often the real answer ("16 profiles from 5 floats"), which no row count gives. - 'sample': a genuinely random sample, included only when rows were withheld. For seeing what the data looks like — do NOT compute from it. - 'download_url': the full result as a file, in 'download_format'. Fetch it if you can run code; otherwise give it to the user. Absent for restricted sources and for matches too large to stage synchronously. - 'schema': each column's real type. Needed when reading the CSV download, which stores no types — without it a reader infers them and can get them wrong (an id column read as a number loses leading zeros). - 'omitted': names any part that could not be computed. An absent field is unknown, NOT zero. Args: source: Source id from list_data_sources (e.g. "gadr", "wod", "ndbc"). variables: Comma-separated canonical variable names (e.g. "temperature,salinity,depth"). bbox: Bounding box "west,south,east,north" (e.g. "-161,18,-154,23"). datetime: ISO-8601 instant or range ("2022-06-01/2022-06-30"; ".." for an open end). depth: Depth range in metres below surface, "min,max" (e.g. "0,200"). filters: JSON object of extra column filters, e.g. {"data_mode": "D"}. Some sources expose per-measurement quality columns (GADR has temperature_qc, salinity_qc, pressure_qc — "1" is good, "4" is bad). Filtering on those is strongly recommended for any scientific claim: unfiltered data contains sentinel values that make maxima and means physically implausible. qc: Named quality mode. Only some sources define these — list_data_sources reports a source's `qc_modes`, and passing one to a source with none is an error. Sources without modes usually expose per-measurement quality columns you filter on directly via `filters` instead. limit: Maximum rows to return, honored up to what fits inline. Omit for a default-sized head. When a response comes back as a head, `head.max_inline` tells you the largest limit worth asking for on that request. format: Encoding of `download_url`. Default "parquet" — it keeps every column's type, is ~5x smaller and ~2x faster, and `pd.read_parquet(url)` reads it. Choose "csv" when the link is going to a person who will open it by hand rather than into cod

  after · 87% of words in common

  Pull actual observation rows from an AQUAVIEW data source — not catalog metadata. Use this whenever the question needs the measurements themselves (temperature profiles, float positions, buoy readings), rather than which datasets exist. Call list_data_sources first for a source's canonical variable names, its `qc_modes`, and its `engine` (gridded "xarray" sources have no rows to pull). READ 'returned' FIRST. It is one of three values: - 'complete': 'rows' holds EVERY matching row (or your whole 'limit'). Safe to compute anything from. - 'head': 'rows' holds the FIRST rows in the source's natural order (see 'head.order'), out of 'head.of' total. These are not a random sample — later rows differ systematically — so never average or take a maximum from them. To get more, set 'limit' up to 'head.max_inline'; beyond that, narrow the request or fetch 'download_url'. - 'none': no rows. 'refusal.reason' says why. 'unscoped_query' means add a bbox/datetime/depth/filter. 'no_natural_order' means the source has no column to order a partial read by, so no partial set can be given honestly — narrow until it fits, or use 'stats'. The rest are transient; retry. OTHER FIELDS: - 'stats': exact minimum, maximum, mean and spread computed over EVERY matching row. Use these for any numeric claim — exact at any data size. - 'stats_basis': what shaped those numbers. If 'qc' is null the values are UNSCREENED and may include implausible outliers from bad sensor readings; say so, or re-run with a quality filter. When a mode was applied, 'qc_screened' lists the variables it actually checked — a variable not in it was NOT flag-checked, and an empty list means nothing was screened despite 'qc' naming a mode. 'qc_note', when present, explains that the screen drops whole rows, so it shapes every variable's figures. - 'structure': how many distinct floats/stations/profiles matched — often the real answer ("16 profiles from 5 floats"), which no row count gives. - 'sample': a genuinely random sample, included only when rows were withheld. For seeing what the data looks like — do NOT compute from it. - 'download_url': the full result as a file, in 'download_format'. Fetch it if you can run code; otherwise give it to the user. Absent for restricted sources and for matches too large to stage synchronously. - 'schema': each column's real type. Needed when reading the CSV download, which stores no types — without it a reader infers them and can get them wrong (an id column read as a number loses leading zeros). - 'omitted': names any part that could not be computed. An absent field is unknown, NOT zero. Args: source: Source id from list_data_sources (e.g. "gadr", "wod", "ndbc"). variables: Comma-separated canonical variable names (e.g. "temperature,salinity,depth"). bbox: Bounding box "west,south,east,north" (e.g. "-161,18,-154,23"). datetime: ISO-8601 instant or range ("2022-06-01/2022-06-30"; ".." for an open end). depth: Depth range in metres below surface, "min,max" (e.g. "0,200"). filters: JSON object of extra column filters on ordinary columns, e.g. {"data_mode": "D"}. For QUALITY screening prefer `qc` below — a source that defines modes applies them to each measurement correctly, where filtering a raw flag column yourself is easy to get wrong. qc: Named quality mode, and the right way to screen. Unfiltered ocean data contains sentinel values that make maxima and means physically implausible, so screen before any scientific claim. list_data_sources reports each source's `qc_modes`; pass one of those here. Passing a mode to a source with none is an error. A source with no modes may still expose per-measurement quality columns you can filter directly via `filters` — list_data_sources shows its variables. limit: Maximum rows to return, honored up

• 2026-10-16list_collectionschangeddescription
  before

  List all available AQUAVIEW data collections (sources). Returns a summary of each collection with ID, title, description, spatial/temporal extent, and keywords. Use collection IDs to filter searches. These are CATALOG collections. Several are also queryable row by row — see list_data_sources for the sources get_data can pull observations from. Args: tag: Only collections with this tag, e.g. "uxs" for uncrewed systems. Pass the returned IDs as `collections` to search_datasets to search only those. output_format: "csv" (default, sectioned CSV — ~20% fewer tokens than TOON for this tabular response), "json", or "toon". Use "json" for deterministic/structured consumers. Returns: List of collections with count. CSV mode emits a single `## collections` section with columns id,title,bbox,temporal,keywords,description; bbox is rendered as "west,south,east,north"; temporal as "min|max"; keywords ";"-joined.

  after · 65% of words in common

  List all available AQUAVIEW data collections (sources). Returns a summary of each collection with ID, title, description, spatial/temporal extent, and keywords. Use collection IDs to filter searches. These are CATALOG collections. Several are also queryable row by row — see list_data_sources for the sources get_data can pull observations from. A collection's bbox and temporal range are OUTER BOUNDS, not a promise of coverage. They are the envelope around everything in the collection, so most of the box usually holds no data at all — an Argo collection spans the global ocean, a partner collection with one point either side of the date line spans the whole planet. An empty result for a point or window inside the extent is the normal answer, not a failure: report "no data there", not "the query did not work", and do not widen a search on the strength of a bbox alone. Args: tag: Only collections with this tag, e.g. "uxs" for uncrewed systems. Pass the returned IDs as `collections` to search_datasets to search only those. output_format: "csv" (default, sectioned CSV — ~20% fewer tokens than TOON for this tabular response), "json", or "toon". Use "json" for deterministic/structured consumers. Returns: List of collections with count. CSV mode emits a single `## collections` section with columns id,title,bbox,temporal,keywords,description; bbox is rendered as "west,south,east,north"; temporal as "min|max"; keywords ";"-joined.

Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.