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

Annotations already declare this as a safe, read-only, idempotent operation. The description adds value by stating the return format: 'Returns attribute rows (and geometry).' It also characterizes the where clause as 'SQL-like,' which provides behavioral insight into query semantics. This exceeds the baseline for annotation-covered tools.

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 at three sentences, front-loaded with the purpose, followed by parameter highlights, return format, and a usage tip. Every sentence earns its place with no redundancy or fluff. The formatting of code-like terms (where, out_fields, etc.) is consistent and readable.

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 full schema coverage, the description provides sufficient context: it ties the url to search_datasets, explains the return format (rows and geometry), and offers a sampling pattern. Even without an output schema, the agent can predict the tool's behavior and integrate it into a workflow. Missing pagination details are available in the schema via offset/limit.

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?

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description reinforces key parameter semantics like 'SQL-like where' and 'comma-separated out_fields,' but these are already present in the schema. It does not introduce additional parameter details beyond what the schema provides, so the score stays at baseline.

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's function: 'Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets).' The verb 'Query' specifies the action, and the resource is explicitly an ArcGIS layer. This distinguishes it from sibling tools like layer_info (metadata) or search_within (spatial search), especially with the reference to search_datasets as a 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?

The description provides clear usage context by indicating the url should come from search_datasets, placing the tool in a workflow. It also gives a practical sampling tip: 'Use where="1=1" + out_fields="*" to sample.' However, it does not explicitly mention when not to use this tool or recommend alternatives like layer_info for metadata, so it falls short of a 5.

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