query_layer

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

With no annotations, the description carries the full burden and succeeds. It discloses automatic pagination, geometry being off by default and WGS84 when enabled, free-text Armenian category values, and numeric air-quality metrics stored as strings—none of which are visible from the 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?

Every sentence earns its place: core purpose, quick-start examples, geometry behavior, vocabulary caveat, and data-type caveat. The phrasing is compact, front-loaded, and free of filler.

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 9-parameter tool with no annotations and no output schema, this description covers the critical operational context: defaults, pagination, geometry, known quirks, and an alternative discovery tool. Nothing essential is missing for an agent to call it correctly.

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 56%, so several parameters are already documented, but the description adds meaning beyond those field descriptions: the dump-all behavior of where='1=1', automatic pagination, and the data-quality caveats. It doesn't document every parameter, but it enriches the most operationally important ones.

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 opens with a specific verb and resource ('Run an attribute query against any layer') and a concrete output ('get rows back as JSON'). It calls itself 'the workhorse,' which, among a large sibling list containing describe_layer, count_features, aggregate, and query_near_point, clearly marks this as the generic row-returning query tool.

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?

It gives explicit usage patterns: where='1=1' to dump a table, a SQL where clause to filter, and return_geometry=true to get WGS84 coordinates. It also points to get_distinct_values when category spellings are uncertain, which is exactly the when-to-use guidance an agent needs instead of guessing.

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