vllm_status

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

With no annotations, the description carries the behavioral burden. It discloses the output scope and the local Intel Docker image nuance, and the status framing implies a read-only operation. However, it does not explicitly state that no state changes occur, nor any other behavioral traits such as permissions or latency.

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?

A single well-structured sentence delivers the full scope of the tool. The core subject is front-loaded, and the parenthetical detail adds meaningful specificity without redundancy.

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 zero-parameter status/read tool, the description adequately conveys what information the agent will receive. A formal return schema or exact JSON shape would improve completeness, but the description gives enough detail to set expectations.

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 baseline is 4. The description correctly focuses on the output rather than parameters, and no parameter compensation is needed.

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 resource ('orchestrator-managed vLLM instances') and enumerates the fields returned (image, port, model, backend id, container), plus the additional serving-stack check. Although it lacks an explicit verb like 'list' or 'get', the intent is evident and it is distinguishable from the lifecycle sibling tools such as start_vllm and stop_vllm.

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 explicit guidance is provided about when to use this tool versus alternatives. There is no mention of checking status before starting/stopping instances or comparing against list_hardware/list_local_models. Usage context is only implied by the tool name and content.

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