_ registry / mcp http-sse · checked 1h ago

AQUAVIEW MCP Server

https://mcp.aquaview.org

Registry code: 3b0d5de3bbc0de29

api record

Search and access global oceanographic and environmental datasets

from a public catalogue that lists it, not from the operator

endpoint
https://mcp.aquaview.org/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is AQUAVIEW MCP Server live?
Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.
Is AQUAVIEW MCP Server free to use?
Yes — the hub reached it with no key and no payment.
What tools does AQUAVIEW MCP Server have?
6 tools: aggregate, search_datasets, get_item, get_data, list_collections, list_data_sources.
Is AQUAVIEW MCP Server safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
99.6%

90 days 99.5%· all time 99.5%

latency
270ms

last good check

priced tools
0

of 6 tools

_ answered our checks, 90 days 599 checks · signed record
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
_ what it is for
topic
weather & environment
used for
  • search oceanographic and marine datasets
  • get dataset download links
  • aggregate dataset counts by region and time
  • list data collections
takes → gives
text, data → data
tools
4 reads
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
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calls served
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successful

paid through this hub
0 USDC

what callers paid

_ what it can do 6 tools
2 open 4 never probed 2 of 6 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • list_collections reads open 8h ago

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "tag": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "output_format": {
      "enum": [
        "csv",
        "json",
        "toon"
      ],
      "type": "string",
      "default": "csv"
    }
  }
}
    arguments 25 lines
  • list_data_sources open 8h ago

    List the AQUAVIEW data sources you can pull actual rows from, or describe one in detail. Call this BEFORE get_data on a source you have not used. Variable names are canonical per source and cannot be guessed — this is where they come from. With an API key, sources your account is entitled to appear here too. Args: source_id: Omit to list every visible source. Give one (e.g. "gadr") to get its full detail: canonical variable names, units, row limits, and filterable columns. output_format: 'csv' (compact, default), 'json', or 'toon'.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "source_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "output_format": {
      "enum": [
        "csv",
        "json",
        "toon"
      ],
      "type": "string",
      "default": "csv"
    }
  }
}
    arguments 25 lines
  • aggregate reads unknown never probed

    Run aggregation queries on the AQUAVIEW catalog — get counts, spatial distributions, temporal distributions, and per-collection breakdowns without fetching individual items. Args: aggregations: Comma-separated aggregation types (required). Valid types: total_count, datetime_max, datetime_min, datetime_frequency, collection_frequency, geometry_geohash_grid_frequency, geometry_geotile_grid_frequency. collections: Comma-separated collection IDs to scope the aggregation (e.g., "NOAA,NDBC"). exclude_collections: Comma-separated collection IDs to omit from the aggregation (e.g., "INCIDENT_NEWS"). Applied as a CQL2 NOT filter, so it composes with `filter`. bbox: Bounding box as "west,south,east,north". datetime: Temporal filter (same format as search_datasets). q: Free-text search to scope the aggregation. Multi-word queries are tokenised on whitespace and OR-combined to mirror search_datasets semantics (aggregate compiles q to CQL2 LIKE clauses on title/description; a raw %multi word% substring match would return 0 for most queries). filter: CQL2-JSON filter as an object or JSON-encoded string. Flat example: {"op": "=", "args": [{"property": "aquaview:institution"}, "NOAA"]}. Nested example: {"op": "<=", "args": [{"property": "aquaview:column_stats_summary.variables.Pressure.min"}, 10]}. license: Comma-separated SPDX license id(s) to scope the aggregation (e.g., "CC-BY-4.0,CC0-1.0"), or one of "public-domain", "proprietary", "various", "unknown". Matched against `aquaview:license`. precision: Grid precision for geo aggregations (1-12, higher = finer grid). Applied to whichever geo aggregation type is requested. datetime_frequency_interval: Interval for datetime_frequency (e.g., "month", "year", "day"). output_format: "csv" (default, sectioned CSV — ~50% fewer tokens than TOON for bucket responses), "json" (structured STAC API response), or "toon". Use "json" for deterministic/structured consumers. Returns: Aggregation results. In CSV mode: one `## <name>` section per aggregation; scalars render on a single line; bucket aggregations render as CSV with a `# rows: N` header, a `# key=value` line for columns that are constant across all rows, and a conventional CSV table of the varying columns. In JSON/TOON mode: the raw SFEOS response with `name`, `data_type`, and `value`/`buckets` for each requested aggregation.

    mcp-tool

    {
  "type": "object",
  "required": [
    "aggregations"
  ],
  "properties": {
    "q": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "bbox": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "filter": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "license": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "datetime": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "precision": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "collections": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "aggregations": {
      "type": "string"
    },
    "output_format": {
      "enum": [
        "csv",
        "json",
        "toon"
      ],
      "type": "string",
      "default": "csv"
    },
    "exclude_collections": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "datetime_frequency_interval": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    }
  }
}
    arguments 123 lines
  • search_datasets reads unknown never probed

    Search the AQUAVIEW catalog — a comprehensive collection of 268K+ global oceanographic, atmospheric, and marine datasets from 15 sources (NOAA, NDBC, IOOS, WOD, CoastWatch, etc.), intended for scientific research, data analysis, and environmental monitoring. This searches METADATA — which datasets exist. To pull the actual observations (temperature profiles, buoy readings, float positions), use get_data instead; list_data_sources shows which sources support it. You must provide at least one of: q, bbox, datetime, collections, filter, or token. Hint: When the user asks for a region, prefer the 'bbox' parameter over including region name in 'q'. Use the geo_hints resource for common bounding boxes. Args: q: Free-text search across title, description, and keywords (e.g., "sea surface temperature"). Case-insensitive, OR logic across terms. bbox: Bounding box as "west,south,east,north" (e.g., "-98,24,-80,31" for Gulf of Mexico). datetime: Temporal filter. Single datetime, range ("2020-01-01T00:00:00Z/2024-12-31T23:59:59Z"), or open-ended ("../2024-01-01T00:00:00Z" or "2020-01-01T00:00:00Z/.."). collections: Comma-separated collection IDs to search within (e.g., "NOAA,NDBC,WOD"). Use list_collections to see available IDs. exclude_collections: Comma-separated collection IDs to omit from results (e.g., "INCIDENT_NEWS" to drop oil-spill reports from free-text searches). Applied as a CQL2 NOT filter, so it composes with `filter`. filter: CQL2-JSON filter as an object or JSON-encoded string. Supports flat and nested property paths. Flat example: {"op": "=", "args": [{"property": "aquaview:institution"}, "NOAA/NCEI"]}. Nested example (filter by per-variable stats summary): {"op": "<=", "args": [{"property": "aquaview:column_stats_summary.variables.Pressure.min"}, 10]}. Supported operators: =, <>, <, >, <=, >=, like, between, in, and, or, not. license: Comma-separated SPDX license id(s) to match exactly (e.g., "CC-BY-4.0,CC0-1.0"). Also accepts the controlled extra values "public-domain", "proprietary", "various", "unknown". Matched against `aquaview:license`, which is normalized on every item regardless of source. sortby: Sort specification as "+field,-field" (e.g., "+properties.datetime,-id"). + = ascending, - = descending. limit: Number of results to return, 1-100. Defaults to 10. token: Pagination token from a previous response's 'next_token' field. Pass this to get the next page of results. fields: Comma-separated list of fields to include in results (e.g., "id,geometry,properties.title"). Reduces response size. When set in CSV mode, emits projected columns (no canonical columns, no asset_keys, no `## assets` section). include_assets: If False (default), asset download records are omitted to save ~60-70% of response tokens; CSV mode still emits a capped `asset_keys` hint column (e.g. `column_stats;overview;gdac_prof;+[N]`) so an agent can see what kinds of assets exist. To get full URLs, call `get_item(collection, item_id)` for the specific id. If True, assets are included inline (full dict for JSON/TOON; `## assets` section with item_id,key,href,type,title rows for CSV). output_format: "csv" (default, sectioned CSV — canonical columns including asset_keys hint), "json", or "toon". Returns: Search results with metadata (total matched, returned, next_token) and items. CSV mode emits `## metadata` + `## items` sections and a `## assets` section when include_assets=True; columns are id,collection,bbox,datetime,title,institution, platform_type,cdm_data_type,variables,keywords,source_url,description, column_stats_summary,geometry,asset_keys. JSON/TOON return the STAC feature shape, with the `assets` dict present only when include_assets=True.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "q": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "bbox": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "limit": {
      "type": "integer",
      "default": 10
    },
    "token": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "fields": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "filter": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "sortby": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "license": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "datetime": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "collections": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "output_format": {
      "enum": [
        "csv",
        "json",
        "toon"
      ],
      "type": "string",
      "default": "csv"
    },
    "include_assets": {
      "type": "boolean",
      "default": false
    },
    "exclude_collections": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    }
  }
}
    arguments 136 lines
  • get_item reads unknown 1h ago

    Fetch a single AQUAVIEW dataset item with full details, properties, and download links (assets). STAC items embed assets inline — each asset includes an href (download URL) and media type, so there is no need for a separate "files" call. Args: collection: The collection ID the item belongs to (e.g., "NOAA", "NDBC", "WOD"). Available from search results or list_collections. item_id: The unique item identifier (e.g., "wod_xbt_ZZ144579"). Available from search results. output_format: "toon" (default) or "json". Returns: Full STAC item with geometry, properties (title, description, datetime, institution, variables, per-variable descriptions under aquaview:variable_descriptions when available, etc.), and assets (download links with media types).

    mcp-tool

    {
  "type": "object",
  "required": [
    "collection",
    "item_id"
  ],
  "properties": {
    "item_id": {
      "type": "string"
    },
    "collection": {
      "type": "string"
    },
    "output_format": {
      "enum": [
        "json",
        "toon"
      ],
      "type": "string",
      "default": "toon"
    }
  }
}
    arguments 23 lines
  • get_data unknown 8h ago

    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 code, or when you can fetch a URL but cannot run code; then use `schema` to set the column types yourself. include_sample: Set False to skip the random sample and return slightly faster. output_format: 'csv' (compact, default), 'json', or 'toon'.

    mcp-tool

    {
  "type": "object",
  "required": [
    "source",
    "variables"
  ],
  "properties": {
    "qc": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "bbox": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "depth": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "format": {
      "enum": [
        "parquet",
        "csv"
      ],
      "type": "string",
      "default": "parquet"
    },
    "source": {
      "type": "string"
    },
    "filters": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "datetime": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "variables": {
      "type": "string"
    },
    "output_format": {
      "enum": [
        "csv",
        "json",
        "toon"
      ],
      "type": "string",
      "default": "csv"
    },
    "include_sample": {
      "type": "boolean",
      "default": true
    }
  }
}
    arguments 106 lines
_ try it through the hub, ceiling 0

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