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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral context by stating the tool returns attribute rows and geometry, and describes the SQL-like query behavior. 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, both essential and informative. First sentence defines purpose and parameters; second provides a concise usage example. No filler, perfectly front-loaded and scoped.

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 complexity (6 params, no output schema, but full annotations), the description covers the core mechanism and return type. It doesn't explain offset/pagination behavior or potential errors, but the schema handles parameter descriptions and the sampling tip addresses common use. Adequate but could be slightly more comprehensive.

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 100%, but the description adds value by characterizing parameters as 'SQL-like where' and 'comma-separated out_fields', and gives a practical sampling tip. These details enhance understanding beyond the raw schema descriptions.

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, enumerates parameters (where, out_fields, order_by, limit, offset), and specifies it returns attribute rows and geometry. It explicitly connects to the sibling tool search_datasets ('from search_datasets'), distinguishing its purpose.

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 implies usage after search_datasets and provides a concrete sampling example ('Use where="1=1" + out_fields="*" to sample'). It does not explicitly state when not to use this tool or mention alternatives like layer_info, but the context is sufficiently clear for an AI agent.

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