mean_center

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

With no annotations, the description must disclose behavioral traits. It only states the computation, but does not mention that it creates new features (out_features), whether it modifies input, or any side effects. The agent lacks awareness of the tool's operational impact.

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 a single sentence, which is concise but at the expense of necessary detail. It is front-loaded with the core purpose but omits crucial information that could fit in a short paragraph.

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 5 parameters and an output schema, the description fails to explain the output or usage patterns. No guidance on when to weight, how case_field works, or the meaning of overwrite. The description is incomplete for effective use.

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 must clarify parameters. It only hints at weight_field via 'optionally weighted,' but ignores overwrite, case_field, in_features, and out_features. The agent gets little help understanding required inputs beyond the names.

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 states the tool computes the geometric mean center of features, optionally weighted. It identifies the specific resource (mean center) and verb (compute), distinguishing it from sibling tools like directional_distribution. However, it could be more explicit and include a brief definition of 'geometric mean center'.

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?

No guidance is provided on when to use this tool vs. alternatives such as directional_distribution or minimum_bounding_geometry. The absence of any usage context or exclusionary language makes it hard for an agent to decide when to invoke it.

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