historian_health

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with '[READ][risk=low]' and 'Pure analysis.' It goes beyond annotations by detailing the return dict structure and verdict values, giving the agent a more complete picture of the tool's behavior.

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 compact with labeled Args and Returns sections, front-loaded safety/risk info, and a practical example. Every sentence contributes useful information 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?

There is no output schema, but the description specifies the complete return dict including all keys and the verdict string. The example further clarifies the expected input format, making the tool self-contained for an agent to call and interpret.

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?

Schema description coverage is 0%, so the description must compensate, and it does. It defines the series format, gap_threshold_s as 'time gap ... counts as a data gap (default 60),' and flatline_eps as 'spread at/below which the series counts as flatline.' This fully documents all three parameters.

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 opens with a specific verb and resource: 'Bad-tag / flatline / gap detection over a provided series.' It clearly distinguishes itself from sibling tools like historian_query by emphasizing 'pure analysis over an injected sample series — no live historian needed.'

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 states the use case: analysis over an injected series, with the important caveat that no live historian is needed. It does not explicitly name alternative tools or provide when-not-to-use guidance, but the context is clear enough for an agent to select it appropriately.

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