directional_distribution

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

With no annotations, the description must cover behavioral traits. It only states the tool creates an ellipse but omits whether it is read-only, destructive, or what side effects occur (e.g., output features created). The agent gets no confidence about safety or authorization needs.

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?

A single sentence, no extraneous text. It is concise but could benefit from a bit more structure or examples without being verbose.

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's complexity (5 parameters, no schema descriptions, and no output schema documentation), the description is too sparse. It does not explain the output (ellipse features), how parameters interact, or what the enum values mean.

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%, and the description adds no parameter-level details. Parameters like ellipse_size (with enum values) and weight_field are not explained, forcing the agent to guess their meaning from names alone.

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 a standard deviational ellipse for point distributions, which distinguishes it from sibling tools like mean_center or hotspot_analysis. However, it lacks a explicit verb like 'calculates' or 'generates', slightly reducing clarity.

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 versus alternatives like mean_center or spatial statistics. The description does not mention prerequisites or typical use cases, leaving the agent to infer from the name alone.

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