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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose one important behavior: 'Blocks on battery unless allow_on_battery is true,' which adds context about conditional execution. However, it does not describe other relevant behaviors such as output format, error handling, or side effects, leaving significant gaps.

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, concise sentence that communicates the core purpose and a key behavioral constraint without extraneous words. It is front-loaded and easy to parse, with no wasted content.

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 8 parameters, 2 required, and no annotation coverage, the one-sentence description is inadequate. It does not explain the meaning of key parameters (mode, prompt, context, etc.), nor does it set expectations for return values despite an output schema existing. The description is too sparse for the tool's complexity.

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 compensate. It only explains the semantics of one parameter (allow_on_battery) by tying it to the blocking behavior. The other seven parameters (mode, model, prompt, context, max_tokens, temperature, system_prompt) are left entirely to the schema, which provides no descriptions. This is insufficient for an 8-parameter tool.

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 action: 'Run either a chat or agent-style local oMLX request,' identifying both the verb and the resource. It distinguishes between two modes (chat/agent), which adds specificity. However, it does not explicitly contrast with the sibling tool omlx_status, so it misses a clear differentiation opportunity.

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 provides no guidance on when to use this tool versus alternatives, nor does it mention the sibling tool omlx_status. It only implies usage by describing what it does. There is no indication of appropriate contexts, prerequisites, or exclusions, leaving the agent to infer 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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. The verb 'Return' implies a read-only operation, but the description does not explicitly state that no modifications are made or add other behavioral context such as scope or side effects. For a simple status tool, this is adequate but not fully transparent.

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 that is direct and free of unnecessary words. It earns its place by stating exactly what the tool does without redundancy or clutter.

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 simplicity (0 parameters, output schema available), the description is complete. It clearly states what is returned, and the presence of an output schema covers return value details. No further context is needed for an agent to select and invoke this tool correctly.

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?

The tool has zero parameters, so the description need not explain parameter meaning. According to the rubric, a baseline of 4 applies when there are no parameters, and the description does not need to compensate for schema gaps since none exist.

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 uses the specific verb 'Return' and clearly identifies the two resources: local oMLX model list and current Mac power status. This makes the tool's purpose unambiguous and distinguishes it from the sibling tool omlx_run, which presumably runs models.

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 when one wants to inspect models or power status, but it does not explicitly state when to use this tool versus omlx_run, nor does it mention any exclusions or prerequisites. It provides context but no direct guidance on alternative selection.

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