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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful non-annotation context: the interpretation rule that an unhealthy status indicates a pipeline defect, not underlying bank risk. It stops short of describing the response shape or any latency/rate considerations.

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, no waste, and the content is front-loaded: what the tool reports first, then the interpretive caveat. Nothing is padded or restated from the title.

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?

There is no output schema, so the description carries the burden of conveying return content, and it does list the concrete facets returned (validation, freshness, missing families, source hashes, fallback status). It does not describe the shape or format of those fields, which is a minor remaining gap for a zero-parameter diagnostic tool.

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 takes no parameters, so there is nothing to disambiguate; the baseline for a zero-parameter tool is 4. The description correctly implies a parameterless, global status check rather than anything scoped.

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?

Names a specific resource (the automated curve pipeline) and states exactly what the status covers: validation, freshness, missing families, source hashes and last-good fallback. This is clearly distinguishable from the analytical siblings (yield_curve, rate_history, bank_analysis), which operate on curve data rather than pipeline health.

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 second sentence implicitly frames the usage case: when a response looks unhealthy, treat it as a pipeline problem rather than bank risk. However, it never explicitly says when to call this tool versus the analytical siblings, nor does it state prerequisites or a recommended call order. Usage is inferable but not stated.

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