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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context: it returns 'attribute rows (and geometry)', uses SQL-like where clauses, and supports pagination via limit/offset. It also hints at sampling behavior. No contradictions 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?

The description is two sentences with the primary action front-loaded. The first sentence states the tool's core function and inputs; the second sentence provides a practical usage tip. No filler or redundant text—every word earns its place.

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 6-parameter tool with no output schema, the description covers the essential aspects: what it queries, how to obtain the URL (via search_datasets), what it returns (attribute rows and geometry), and a simple usage example. It omits details like pagination response structure or error behavior, but the schema and annotations fill most gaps. The reference to sibling usage adds context.

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 baseline is 3. The description repeats parameter semantics already in the schema (e.g., 'SQL-like where', 'comma-separated out_fields'), adding no new per-parameter meaning. The sampling tip (where='1=1' + out_fields='*') is a useful combination but does not elevate semantic understanding beyond 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 action: 'Query an ArcGIS Feature Service / Map Service layer by its url'. It specifies the resource type and distinguishes the tool from siblings like search_datasets (discovery) and layer_info (metadata) by focusing on returning 'attribute rows (and geometry)'. The parameter list reinforces 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 explicitly mentions that the URL comes 'from search_datasets', establishing a prerequisite workflow. The sampling tip ('Use where="1=1" + out_fields="*" to sample') provides a concrete use case. However, it does not explicitly state when to use this tool versus alternatives like layer_info or search_within, so it's clear but not exhaustive.

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