measure_entities

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

Since there are no annotations, the description must carry the full behavioral load. It provides crucial operational context that is not in the schema: the coordinate system (metres) and the post-action (clears the selection).

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 highly efficient, front-loading the main purpose and then logically progressing to detail the measurement outcomes and coordinate system before ending with the system state change (clearing selection).

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 that entity specifications are highly technical, the description is maximally complete by defining the expected coordinate units and outlining the exact data returned based on input count (1 vs 2 entities).

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 100% schema coverage, the description serves primarily to reinforce the complex string format defined in the schema and clarify the derived data (e.g., that one edge yields length/radius).

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 identifies the specific function (measure entities) and enumerates the resources it acts upon (faces, edges, vertices). It distinguishes its operation by specifying the output for single vs. multiple entities.

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 implies the usage context (needing a measurement) but does not explicitly guide the agent on when to use it over alternative dimension or analysis tools present in the sibling list.

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