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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds that the tool 'Returns attribute rows (and geometry)', disclosing the output format. It also gives a practical sampling tip, providing behavioral context not present in the schema or 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?

The description is two sentences, front-loaded with purpose and parameters, followed by a practical usage tip. Every word contributes value, with 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?

With a comprehensive schema (100% coverage) and rich annotations, the description fills key gaps by stating the return type (attribute rows and geometry) and referencing the search_datasets workflow. It adequately covers the tool's behavior for a query operation, though no output schema exists.

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% with descriptive parameter definitions. The description lists parameter names and some semantics (e.g., 'SQL-like where', 'comma-separated out_fields') but largely echoes schema information, adding little new meaning beyond what the schema already provides.

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 explicitly states the verb 'Query' and the resource 'ArcGIS Feature Service / Map Service layer', making the tool's function immediately clear. It distinguishes itself from siblings like search_datasets by specifying it operates on a layer URL and returns attribute rows/geometry, not dataset discovery.

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 provides contextual guidance by noting the URL comes from search_datasets, implying a typical workflow. The tip to use `where="1=1"` and `out_fields="*"` to sample gives a concrete usage scenario, though it does not explicitly name alternative tools or exclusion criteria.

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