geoparquet_aggregate_attribute

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and excels. It discloses that grouping executes server-side ('only the group rows cross the network'), that a non-numeric measure is 'refused before any byte is fetched', that omitting the rectangle reads the full dataset, and that results are 'ordered by the aggregate descending'. It even lists the complete return payload (groups, rows_aggregated, truncated, sql, scan), which is behavioral disclosure beyond the structured schema.

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 long but densely organized into four labeled sections (overview, WHEN TO USE IT, PARAMETERS, WHAT COMES BACK) with one semantic fact per parameter line. No sentence is filler — even asides like 'which counts rows' clarify a conditional rule. The length is proportionate to the tool's genuine complexity (9 params, 5 aggregate modes, spatial constraints).

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 complex 9-parameter tool with zero schema descriptions and zero annotations, the description covers everything needed to invoke it correctly: operation, selection criteria, per-parameter semantics, relational constraints between parameters, performance trade-offs, and return shape. The output schema exists and is also described in prose, which is belt-and-suspenders 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 0%, so the description must fully compensate, and it does: every one of the 9 parameters gets semantic meaning beyond type/default. It explains the conditional relationship between aggregate and measure ('Required for sum, avg, min and max, and rejected for count'), the 'Pass all four or none' invariant for the lon/lat box, and the ordering semantics of limit.

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 opening sentence states a specific verb and resource: 'Group the rows of a dataset by one column and aggregate them', with the distinctive detail that grouping runs remotely. It is explicitly differentiated from the sibling geoparquet_filter_spatial ('Reach for it before geoparquet_filter_spatial whenever counting would do'), so an agent can tell them apart without opening schemas.

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?

A dedicated 'WHEN TO USE IT' section gives concrete query shapes ('how many of each', 'what is the average', 'which is the most common') and states the decision rule against a named alternative: 'Reach for it before geoparquet_filter_spatial whenever counting would do.' It also warns about the expensive full-dataset path ('slow and expensive on a multi-gigabyte source'), which implicitly tells the agent when to add a bounding box.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.