local_ai_status

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

No annotations provided, so the description carries the full burden. It discloses the type of information returned (model, install state, chunk counts), but does not mention side effects (e.g., read-only nature), authorization needs, or rate limits. Basic transparency is achieved, but not exhaustive.

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?

Single sentence, front-loaded with 'On-device embedding status', no extraneous words. Every part is informative and necessary.

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 zero parameters and no output schema, the description sufficiently covers the tool's purpose and output. It explains the key components of the status. However, it could optionally mention that it is a read-only query for completeness.

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?

No parameters exist (0 params, 100% schema coverage). The description does not need to add parameter information. Baseline 4 is appropriate as there is nothing to improve.

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 tool's output: on-device embedding status with model, install state, and chunk counts. It uses specific resource terms ('model', 'install state', 'chunks') and is distinct from sibling tools that handle searching, history, or modifications.

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 on when to use this tool versus alternatives. The description offers no context about prerequisites, scenarios, or exclusions, leaving the agent without decision support for selection.

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