get_attrib_stats

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

With no annotations provided, the description carries the behavioral disclosure burden. It clearly discloses that the tool aggregates numeric statistics, and it adds a non-obvious behavioral nuance: vector attributes report per-component ranges. It could also mention handling of non-numeric attributes or failure cases, but the provided behavioral context is strong.

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 compact, well-structured, and every sentence adds value: the operation, the usage rationale, sibling differentiation, vector behavior, and parameter documentation. The 60k-cache example is vivid but earns its place by illustrating the tool's purpose.

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 simple parameter set and no output schema, the description covers the core outcome—statistics values and vector per-component behavior—as well as the full parameter semantics and usage context. It does not specify the exact return format, but for a stats tool this is a minor gap, not a completeness failure.

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 0%, so the description fully compensates by documenting all three parameters: node_path, attribs, and attrib_class. It clarifies that attribs can be omitted to select every attribute of the class and enumerates valid attrib_class values. This is complete parameter-level guidance beyond the bare 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 opens with a specific verb and resource: 'Aggregate statistics for numeric attributes: min, max, mean, sum.' It explicitly distinguishes itself from siblings like get_geometry_info and get_attrib_values, so an agent knows exactly what this tool does and how it differs.

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 states when to use this tool ('Use this to prove something is happening') and contrasts it with alternatives, explaining why get_geometry_info and get_attrib_values are less appropriate, including a concrete cache-size example. This is explicit routing guidance rather than leaving the agent to infer.

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