AQUAVIEW MCP
Server Details
AQUAVIEW MCP is a hosted Model Context Protocol server that gives any LLM agent direct access to 700,000+ oceanographic, atmospheric, and marine datasets from 68 authoritative sources — NOAA, IOOS, World Ocean Database, Argo, GOES-R, NEXRAD, ESA Sentinel, and more. Ask in plain English; the server returns structured results with direct download links for NetCDF, GRIB2, GeoTIFF, and CSV files.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Full call logging
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Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.2/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: aggregating data, fetching a single item, listing collections, and searching datasets. There is no overlap or ambiguity.
Three tools follow the verb_noun pattern (get_item, list_collections, search_datasets), but 'aggregate' is a verb alone without a noun, causing minor inconsistency. Overall pattern is still clear.
Four tools is a reasonable number for a catalog service, covering essential operations without being overly sparse or bloated.
The tool set covers listing collections, searching datasets, fetching details, and running aggregations. For a read-only catalog, this is fairly complete; missing operations like update/delete are not expected.
Available Tools
4 toolsaggregateAInspect
Run aggregation queries on the AQUAVIEW catalog — get counts, spatial distributions, temporal distributions, and per-collection breakdowns without fetching individual items.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | 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). | |
| bbox | No | Bounding box as "west,south,east,north". | |
| filter | No | 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]}. | |
| datetime | No | Temporal filter (same format as search_datasets). | |
| precision | No | Grid precision for geo aggregations (1-12, higher = finer grid). Applied to whichever geo aggregation type is requested. | |
| collections | No | Comma-separated collection IDs to scope the aggregation (e.g., "NOAA,NDBC"). | |
| aggregations | Yes | 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. | |
| output_format | No | "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. | csv |
| exclude_collections | No | Comma-separated collection IDs to omit from the aggregation (e.g., "INCIDENT_NEWS"). Applied as a CQL2 NOT filter, so it composes with `filter`. | |
| datetime_frequency_interval | No | Interval for datetime_frequency (e.g., "month", "year", "day"). |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden, and it adequately discloses key behaviors: compiling q to CQL2 LIKE clauses, tokenization semantics, output format options, and grid precision usage. It does not mention auth or rate limits, but these are less critical for a read-only aggregation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core purpose and specifies the types of aggregations. Every phrase earns its place, and no extraneous information is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, output schema), the description covers the primary use case and result types. It could mention that return values are aggregated (but output schema covers this). Slightly above average completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The tool description adds no additional parameter information beyond what the schema already provides; it does not restate or expand on parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool's purpose: running aggregation queries on the AQUAVIEW catalog to obtain counts, spatial/temporal distributions, and per-collection breakdowns without fetching items. This clearly distinguishes it from siblings like search_datasets (which returns individual items) and list_collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage for aggregate statistics as opposed to fetching items, but lacks explicit guidance on when not to use it or alternatives. However, the sibling tools are sufficiently different, making the context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_itemAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | The unique item identifier (e.g., "wod_xbt_ZZ144579"). Available from search results. | |
| collection | Yes | The collection ID the item belongs to (e.g., "NOAA", "NDBC", "WOD"). Available from search results or list_collections. | |
| output_format | No | "toon" (default) or "json". | toon |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds useful behavioral context: that STAC items embed assets inline with href and media type. It does not describe error handling or side effects, but for a read-only fetch it is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. Front-loaded with purpose and key information. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description sufficiently covers the tool's behavior, parameters, and return value (assets inline). Context within the toolset is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds minor value by noting item_id and collection are available from search results. No additional parameter semantics beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single AQUAVIEW dataset item with full details, properties, and download links (assets). It distinguishes from siblings by noting assets are embedded inline, eliminating the need for a separate files call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving item details and download links, and indicates no separate tool is needed for files. However, it does not explicitly compare to sibling tools (e.g., search_datasets) or provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_collectionsAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| output_format | No | "csv" (default, sectioned CSV — ~20% fewer tokens than TOON for this tabular response), "json", or "toon". Use "json" for deterministic/structured consumers. | csv |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. It states the tool returns a summary with specific fields, but does not disclose read-only nature, potential side effects, or limits. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no wasted words. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists, the description sufficiently covers purpose, output details, and usage intent for a listing tool with one optional parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter description (enum values, token efficiency). The tool description adds no extra parameter context, so baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List all available AQUAVIEW data collections (sources)' with specific verb and resource. It distinguishes from sibling tools (aggregate, get_item, search_datasets) by focusing on collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use collection IDs to filter searches', providing a direct usage context. It lacks explicit when-not-to-use or alternatives, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_datasetsAInspect
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text search across title, description, and keywords (e.g., "sea surface temperature"). Case-insensitive, OR logic across terms. | |
| bbox | No | Bounding box as "west,south,east,north" (e.g., "-98,24,-80,31" for Gulf of Mexico). | |
| limit | No | Number of results to return, 1-100. Defaults to 10. | |
| token | No | Pagination token from a previous response's 'next_token' field. Pass this to get the next page of results. | |
| fields | No | 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). | |
| filter | No | 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. | |
| sortby | No | Sort specification as "+field,-field" (e.g., "+properties.datetime,-id"). + = ascending, - = descending. | |
| datetime | No | 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 | No | Comma-separated collection IDs to search within (e.g., "NOAA,NDBC,WOD"). Use list_collections to see available IDs. | |
| output_format | No | "csv" (default, sectioned CSV — canonical columns including asset_keys hint), "json", or "toon". | csv |
| include_assets | No | 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). | |
| exclude_collections | No | 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`. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes search behavior, output formats, and pagination, but does not explicitly state that it is a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with purpose, then requirement and hint. No unnecessary information, each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 12 parameters and an output schema, the description covers critical context (catalog, sources, usage hints, parameter requirement). It could mention pagination explicitly but schema covers token details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context beyond schema (catalog scope, geo_hints hint, minimum parameter requirement), but does not significantly enhance per-parameter understanding beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches the AQUAVIEW catalog with specific sources and purpose, distinguishing it from siblings like aggregate, get_item, and list_collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists required parameters and provides hints about preferring 'bbox' over including region in 'q'. Does not explicitly state when not to use, but sibling differentiation is clear from purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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