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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that it returns attribute rows and geometry, and that the query is SQL-like. It doesn't mention potential errors or rate limits, but the annotation bar is low, and the added context is useful.

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 (three sentences), front-loaded with the core action, and every sentence adds value—action, parameters, return type, and a usage tip. No fluff or repetition.

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 read-only query tool with six parameters and full schema coverage, the description sufficiently covers the main behavior, return value, and a key usage pattern. It doesn't explain response structure in detail, but the absence of an output schema and the richness of the schema make this acceptable.

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 schema already explains each parameter. The description reiterates the parameter names and adds the 'SQL-like' framing and sampling tip, but these do not significantly deepen understanding of individual parameters 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 queries an ArcGIS Feature/Map Service layer by URL, using SQL-like parameters. It distinguishes itself from siblings like search_datasets (which finds datasets) and layer_info (which likely describes metadata) by specifying the query action and its data source.

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 directs the agent to source the URL from search_datasets, which implies a workflow sequence. It also gives a practical sampling tip with where='1=1' and out_fields='*'. However, it does not explicitly mention when to avoid this tool or contrast it with alternatives, so it loses one point.

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