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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar for extra value is lower. The description adds useful behavioral detail by stating it "Returns attribute rows (and geometry)" and by recommending a sampling mode. No contradiction 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?

Every sentence earns its place: purpose, parameter list, return type, and a practical sampling example. The description is front-loaded with the operation and contains no filler or redundant restatement.

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 six parameters and no output schema, the description covers the URL source, parameter families, return shape, and sampling usage. It does not detail the response envelope or error behavior, but the schema's examples and parameter descriptions fill most of that gap.

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 adds light framing like "SQL-like where" and "comma-separated out_fields", but the schema already documents each parameter with examples. The added semantic value is real but modest.

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 names a specific verb and resource: "Query an ArcGIS Feature Service / Map Service layer" and identifies where the url comes from (search_datasets). This clearly distinguishes it from siblings like search_datasets (finding datasets) and layer_info (inspecting 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?

It provides clear context: use this tool after search_datasets has yielded a layer URL, and it gives a concrete sampling recipe (where=1=1 + out_fields=*). It does not explicitly name alternatives or state when not to use it, so it stops 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.