probe

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

With no annotations, the description carries the full behavioral burden. It discloses the return metrics and implies a read-only health check, but does not explicitly state that it does not modify state, nor does it mention failure modes, authentication needs, or side effects. This is minimally acceptable but not thorough.

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, well-front-loaded sentence that conveys the tool's purpose and output. No extraneous words; every phrase earns its place.

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 only one parameter and no output schema, the description sufficiently covers what the tool does and what it returns. It lacks behavioral details like idempotency or error behavior, but for a straightforward health probe, it is largely complete.

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 sole parameter 'config' is fully described in the schema (100% coverage). The description adds no additional meaning beyond the schema's 'path to probes.yml config file'. At baseline 3, the description does not enhance parameter understanding.

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: running a health check against configured LLM endpoints, and lists the metrics returned (TTFT, latency, throughput, health status). While the purpose is specific and actionable, it does not explicitly distinguish from the sibling tool 'check_model', which could cause ambiguity.

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. It does not mention prerequisites, context, or conditions for appropriate use, leaving the agent without decision-making support for tool selection.

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