pa_predict

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return values (approval likelihood, confidence, etc.) but doesn't mention important behavioral aspects like whether this is a read-only operation, if it requires specific permissions, rate limits, or how the prediction is generated (e.g., based on historical data, machine learning model).

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 efficiently structured in a single sentence that immediately states the purpose and enumerates the return values. Every element serves a clear purpose with no redundant information, making it easy to parse and understand quickly.

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?

For a prediction tool with 5 parameters, 80% schema coverage, and no output schema, the description adequately covers the purpose and return values but lacks details on behavioral context, error handling, and when to use versus alternatives. Given the complexity and absence of annotations, it should provide more guidance on limitations or assumptions.

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 80%, providing good documentation for most parameters. The description doesn't add significant parameter semantics beyond what the schema already provides, though it does contextualize the parameters as inputs for 'predicting prior authorization approval probability.' This meets the baseline for high schema coverage.

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 specific action ('Predict prior authorization approval probability') and resource ('for a procedure'), distinguishing it from sibling tools like 'pa_status' which likely checks status rather than predicting probability. It precisely defines what the tool does without being vague or tautological.

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 implies usage in prior authorization contexts but doesn't explicitly state when to use this tool versus alternatives like 'pa_status' or other clinical tools. No guidance is provided on prerequisites, exclusions, or specific scenarios where this prediction is most appropriate.

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