oncofiles

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the return values (status, trend direction, etc.) and the tool's design intent, which adds useful context. However, it lacks details on potential limitations, such as data freshness, error handling, or performance characteristics, which would be beneficial for a tool with no 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 front-loaded with the core purpose in the first sentence, followed by details on returns and design intent in subsequent sentences. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 the tool has 0 parameters, an output schema exists, and no annotations, the description is largely complete—it explains the purpose, returns, and usage context. However, it could be more comprehensive by addressing potential behavioral aspects like data sources or update frequency, though the output schema may cover return values, reducing the need for such details.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not mention any parameters, which is appropriate. It adds value by explaining the output semantics (e.g., 'status', 'trend direction'), compensating for the lack of parameter info, though this is more about output than input.

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's purpose with specific verbs ('Get a summary') and resources ('latest value for every tracked lab parameter'), and distinguishes it from siblings like 'get_lab_time_series' or 'get_lab_trends' by focusing on a comprehensive overview rather than detailed analysis. It explicitly mentions what it returns (status, trend direction, etc.), making its function unambiguous.

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 usage ('Designed as a quick overview for clinical decision support'), indicating when to use this tool—for a rapid summary rather than in-depth analysis. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'analyze_labs' or 'compare_labs', which could enhance differentiation.

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