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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context by stating that it returns attribute rows and geometry, and the sampling hint clarifies how to make safe exploratory queries. No contradiction with annotations.

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 the core purpose and parameter list, followed by a practical usage tip. Every clause adds value; no filler or redundancy.

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 no output schema, the description still communicates the return shape (attribute rows and geometry), covers pagination via offset/limit, filtering via where, and field selection via out_fields. Combined with the rich schema examples, it provides enough context to invoke 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?

The input schema covers all six parameters with descriptions and examples (100% coverage). The description reinforces the SQL-like semantics and the out_fields concept but adds little beyond the schema's own parameter explanations, so a baseline of 3 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 it queries an ArcGIS Feature/Map Service layer by URL, with a specific list of SQL-like query parameters. This distinguishes it from sibling tools like layer_info (likely metadata) and search_within (searching data) by naming the exact resource and operation.

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 explicitly references that the URL comes from search_datasets and gives a concrete sampling pattern ('where=1=1 + out_fields="*"'). This provides clear usage context, though it does not explicitly contrast with sibling tools like layer_info.

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