queue_status

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. It effectively describes what the tool returns (queue status, loaded models, pending requests, GPU memory usage) and its monitoring/debugging purpose. However, it lacks details on potential side effects (e.g., if it's read-only, performance impact, or rate limits), which would be valuable given the absence of annotations.

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 well-structured and concise, with three short paragraphs that front-load the purpose, provide usage context, and specify the return format. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 (monitoring system status), no annotations, and the presence of an output schema (which handles return value details), the description is complete. It covers the tool's purpose, usage scenarios, and high-level output structure, leaving technical specifics to the output schema, which is appropriate.

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 0 parameters, and the schema description coverage is 100%, so no parameter documentation is needed. The description appropriately focuses on the tool's purpose and output without unnecessary parameter details, earning a high baseline score for this dimension.

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's purpose with a specific verb ('Get') and resource ('current status of the model queue system'). It distinguishes itself from siblings like 'health' (general system health), 'models' (likely listing models), and 'get_model_info_tool' (specific model details) by focusing exclusively on queue status, loaded models, and queued requests.

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 clear context for when to use the tool ('Useful for monitoring queue performance and debugging loading issues'), which helps differentiate it from siblings. However, it does not explicitly state when NOT to use it or name specific alternatives (e.g., 'health' for broader system status), keeping it from a perfect score.

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