Get Field Statistics

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

Annotations already include readOnlyHint=true, confirming read-only behavior. The description adds value by disclosing that for non-numeric fields it returns count and distinct values, which is specific behavioral context beyond the annotation. No contradiction.

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?

Two concise sentences with no unnecessary words. The key action and conditional behavior are stated upfront, making it easy to parse.

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?

Given the tool has an output schema covering return values, the description is sufficient. It explains the types of statistics computed and the fallback for non-numeric fields, leaving no missing 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?

With 0% schema description coverage, the description partly compensates by indicating field_name should be numeric. However, it does not explain layer_id or provide any additional parameter details beyond what is minimally inferable from the 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 tool computes aggregate statistics for numeric fields and provides specific examples (count, sum, mean, min, max, stdev). It also explains behavior for non-numeric fields (count and distinct values). This distinguishes it from siblings like get_unique_values or get_layer_features.

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 implies usage for obtaining aggregate statistics but does not explicitly specify when to use this tool versus alternatives like get_unique_values or identify_features. No guidance on when not to use it or context where it might be unsuitable.

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