local_models_status

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only, non-side-effect nature is covered. The description adds valuable context about what is revealed (VRAM placement, competing model) and why it matters. It doesn't describe return format, but for a status tool that's acceptable.

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?

Two sentences: first states what it does, second provides when and why. Information is front-loaded and every phrase adds value. No redundancy or fluff.

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?

For a parameterless status tool with no output schema, the description covers everything an agent needs: what it shows, when to call it, and the diagnostic benefit. There are no missing prerequisites or side-effect caveats to document.

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 and the schema coverage is 100% (empty schema). Per rubric, baseline is 4. The description appropriately focuses on purpose rather than parameters, which are nonexistent.

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 a specific verb ('Show') and resource (active local profile, model per tier, Ollama loaded state). It precisely differentiates from sibling tools, which all perform different actions (delegate, review, summarize, index, search). The scope is unambiguous.

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?

Explicitly states when to use: when a local tool is slow or fails, or before a batch of local work. It explains the diagnostic value (VRAM placement, competing model). This is strong guidance with clear triggers and purpose, even though it doesn't name alternatives, since siblings are distinct tasks.

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