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

There are no annotations, so the description bears full responsibility for behavioral disclosure. It does not explain what 'predict' entails (e.g., whether it's read-only, what the output format is, or any side effects). It simply restates the action without adding behavioral context.

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, concise sentence with no wasted words. It is front-loaded with the core action and resource.

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?

The tool has three parameters and no output schema, but the description only vaguely defines the purpose. It does not explain what 'status' means, how entity_id relates, or what the response looks like, leaving significant gaps for an agent to infer.

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?

With only 33% schema description coverage, the description adds meaning to 'article' (specific article) and 'horizon_days' (N days), but leaves 'entity_id' unexplained. It partially compensates for the schema gaps but does not fully cover the third parameter.

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 tool predicts the status of a specific DORA article over a time horizon. It uses a specific verb ('Predict') and resource ('status of a specific DORA article'), distinguishing it from sibling tools like predict_entity or predict_score.

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 clear context for when to use the tool: when predicting article status over a number of days. It does not explicitly mention alternatives or exclusions, but the use case 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.