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.
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Average 4.5/5 across 4 of 4 tools scored.
Each tool targets a distinct operation: listing collections, searching datasets, fetching item details, and running aggregations. No functional overlap.
All tool names follow a consistent verb_noun snake_case pattern: list_collections, search_datasets, get_item, aggregate.
Four tools cover the core operations of a catalog server without being excessive or insufficient. The scope is well-defined.
The tool set provides a complete read-only interface: list collections, search with filters, retrieve items with assets, and aggregate statistics. No obvious gaps.
aggregateAInspectRun 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.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| bbox | No | ||
| filter | No | ||
| license | No | ||
| datetime | No | ||
| precision | No | ||
| collections | No | ||
| aggregations | Yes | ||
| output_format | No | csv | |
| exclude_collections | No | ||
| datetime_frequency_interval | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool's behavior: it runs aggregations, composes q as CQL2 LIKE clauses, and returns various output formats. No destructive behavior is implied, so transparency is good but could mention read-only nature explicitly.
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 well-structured with a summary, parameter list, and return format explanation. It is front-loaded with purpose. However, it is somewhat lengthy due to detailed parameter explanations; could be slightly more concise.
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 (11 parameters, multiple aggregation types, output formats), the description covers all necessary aspects: what each parameter does, valid values, return format details, and behavioral notes. It is complete for an aggregation tool.
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 0%, but the description compensates thoroughly by explaining all 11 parameters with valid values, examples, and behavioral notes (e.g., q tokenization, output_format default). This adds significant meaning beyond the 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 runs aggregation queries on the AQUAVIEW catalog, providing counts, spatial/temporal distributions, and per-collection breakdowns without fetching individual items. This distinguishes it from siblings like search_datasets (item retrieval) and list_collections (collection listing).
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 explains when to use the tool for aggregations instead of fetching items. It provides parameter details and notes on composition with filters. However, it does not explicitly state when not to use it or directly compare with siblings for exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_itemAInspectFetch 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, etc.), and assets (download links with media types).
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | ||
| collection | Yes | ||
| output_format | No | toon |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses return structure (STAC item with geometry, properties, assets) and output_format default. Does not mention auth or destructive behavior, but fetch implies read-only.
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?
Well-structured with Args and Returns sections. Sentences are informative, though the line about STAC embedding assets could be considered slightly extra but adds value. Overall efficient.
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 3 params, 0% coverage, no annotations, but has output schema, description is complete. Explains all params and return schema. Could mention error handling but not critical.
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 0%, but description thoroughly explains each parameter: collection (with examples), item_id (with examples), output_format (enum values and default). Adds significant meaning 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?
Description clearly states 'Fetch a single AQUAVIEW dataset item with full details, properties, and download links.' This distinguishes it from siblings like list_collections and search_datasets.
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?
Provides guidance on obtaining collection and item_id from search results or list_collections, and explains no need for separate files call. However, lacks explicit when-not-to-use or alternatives beyond implied context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_collectionsAInspectList 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.
Args: 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.
| Name | Required | Description | Default |
|---|---|---|---|
| output_format | No | csv |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It describes the return structure in detail (CSV columns, format for bbox/temporal/keywords) and notes token efficiency. No contradictions; transparent about read-only listing.
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?
Description is structured into paragraphs with Args/Returns sections. All sentences add value, though slightly verbose in explaining output format. Front-loaded with main purpose; could be trimmed slightly but remains 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?
For a simple tool with one parameter and existing output schema, the description covers selection criteria (what collections are) and invocation (format options). Leaves out details for JSON/TOON responses, but output schema presumably fills the gap.
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?
The single parameter 'output_format' is fully explained with enum values, default, and trade-offs (e.g., '~20% fewer tokens than TOON'). Schema coverage is 0%, but the description compensates excellently, making the parameter easy to choose.
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 specifies 'list all available AQUAVIEW data collections' and details the returned fields (ID, title, spatial/temporal extent, etc.). It distinguishes from siblings like get_item and search_datasets by focusing on enumeration of sources.
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?
Provides guidance on using collection IDs to filter searches and advises on output_format selection (e.g., 'Use json for deterministic/structured consumers'). Lacks explicit when-not-to-use or alternative tools, but offers sufficient context for common use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_datasetsAInspectSearch 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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| bbox | No | ||
| limit | No | ||
| token | No | ||
| fields | No | ||
| filter | No | ||
| sortby | No | ||
| license | No | ||
| datetime | No | ||
| collections | No | ||
| output_format | No | csv | |
| include_assets | No | ||
| exclude_collections | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and excels: it explains token savings with include_assets=False, the effect of fields parameter, output format details (CSV sections, JSON/TOON shape), and that full asset URLs require a separate call to get_item. No contradictions.
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 well-structured: a concise purpose statement, a required-parameter hint, then a detailed parameter list, and a returns section. It is somewhat lengthy but every sentence adds value. Could be slightly more efficient, but overall good.
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 13 parameters, no schema descriptions, and an existing output schema, the description covers all parameters, edge cases (open-ended datetime, CQL2 examples, CSV mode details), and references siblings (list_collections, get_item). The return format is detailed, making it complete for correct tool invocation.
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 0%, so the description fully compensates. Every parameter is explained with format, examples, defaults, and constraints (e.g., bbox 'west,south,east,north', datetime range syntax, CQL2 filter syntax, sortby syntax, license values). This adds critical meaning beyond the raw 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's purpose: searching the AQUAVIEW catalog of oceanographic datasets. It specifies the catalog scope (268K+ datasets from 15 sources) and intended use (scientific research, data analysis, environmental monitoring). This distinguishes it from siblings like get_item (single dataset) or list_collections (listing 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 provides explicit guidance: 'You must provide at least one of: q, bbox, datetime, collections, filter, or token.' It also offers a hint on preferring bbox over q for region queries and references the geo_hints resource. It mentions using list_collections for collection IDs. However, it does not directly contrast with aggregate (e.g., when to use aggregation vs search).
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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