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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the tool's safety profile is clear. The description adds value by disclosing the return type ('Returns attribute rows (and geometry)') and clarifying the SQL-like semantics of the query parameters, which goes beyond the structured 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 the core verb, and every word earns its place. It succinctly states what it does, lists the key parameters, indicates the return type, and gives a practical sampling tip without 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?

For a query tool with 6 parameters, 1 required, and no output schema, the description covers the essential aspects: purpose, URL source, parameter list, return behavior, and a sample usage pattern. It positions the tool within the sibling workflow (search_datasets -> query_layer) and is fully sufficient for an agent to select and invoke it correctly.

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 schema already documents each parameter. However, the description adds meaningful context by grouping the parameters into a coherent query pattern (SQL-like where, comma-separated out_fields, order_by, limit, offset) and offering a sample idiom for using them together. This exceeds the baseline for high schema coverage.

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 opens with a specific verb ('Query') and clearly identifies the resource (an ArcGIS Feature Service / Map Service layer) and the source of the URL (from search_datasets). This distinguishes it from sibling tools like search_datasets (finding datasets) and layer_info (layer metadata).

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 gives clear context: this tool is for querying a layer URL obtained from search_datasets, implying a workflow step. It also provides a practical usage tip ('Use where="1=1" + out_fields="*" to sample'). It stops short of explicitly contrasting with alternatives, but the reference to search_datasets and the tool's name effectively communicate when to use it.

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