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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds useful behavioral detail beyond that, such as the specific backend dependencies checked and the lazy-loading behavior of the embedding model on first sentiment search. No contradiction with 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 a single, well-structured sentence that front-loads the core purpose and then efficiently enumerates the specific health checks. Every clause adds useful information without 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?

Given zero parameters, simple invocation, and strong readOnly/idempotent annotations, the description is complete enough for an agent to select and call the tool correctly. It specifies what is measured and the notable lazy-loading behavior, even without an output schema.

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 there is no parameter semantic burden for the description. The description explains the tool's scope without needing to document inputs, matching the baseline expected for a parameterless tool.

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 what the tool reports: dependency health of the travel vertical service, listing specific dependencies (elasticsearch, postgres, redis) and the embedding model state. It is distinct from sibling tools like search_destination_sentiment or list_verticals, though it does not explicitly name a differentiating sibling.

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. There is no mention of typical scenarios, prerequisites, or exclusions, which leaves the agent to infer that this is for checking service dependency health.

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