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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is safe. The description adds that it returns attribute rows and geometry, and the sampling tip illustrates safe exploration. No contradictions.

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 action, then compactly lists the main parameters and a practical tip. No wasted words.

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?

Despite 6 parameters and no output schema, the description covers the source of the URL, return payload, and a key usage pattern. With schema covering parameter specifics, this is sufficient for a read-only query tool.

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 covers 100% of parameters with descriptions. The description adds context that parameters are SQL-like, out_fields are comma-separated, and provides a concrete example of using where and out_fields together for sampling, enhancing 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 it queries an ArcGIS Feature/Map Service layer by URL, and explicitly ties the URL source to search_datasets, distinguishing it from siblings like search_datasets (finding) and layer_info (metadata). It specifies SQL-like parameters and return type.

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 tells the agent the URL comes from search_datasets, creating a clear workflow ('use this after search_datasets'). It also provides a sampling tip with where='1=1' and out_fields='*', but doesn't explicitly name alternative tools or when-not-to-use scenarios.

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