ldbd_review_my_track_record

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

No annotations are provided, so the description carries full burden. It transparently discloses authentication requirement, data scoping, exclusion of open predictions, and absence of interpretation/advice. It also enumerates exact output components, including counts and aggregates, making behavior highly predictable.

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?

Although the description is a single long paragraph, it is densely informative with no wasted words. Each clause introduces a distinct, useful aspect: data scope, contents, exclusions, and output philosophy. The semicolon-separated structure keeps related information together.

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 must explain return contents. It does so thoroughly, covering summary stats, recent judgments, biggest misses, and machine aggregates with sample counts. The description is complete enough for an agent to know exactly what data to expect and how to use it.

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 covers 100% of parameters with descriptions including defaults and max values. The tool description mentions 'recent judged predictions' and 'biggest misses' which map to the two parameters, but does not add syntax or format details beyond schema. Baseline 3 is appropriate.

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?

Description names a specific action ('Review your OWN resolved LDBD prediction history') and resource, immediately distinguishing it from sibling tools that list open predictions or show stats. It clearly communicates the self-reflection use case and differentiates from get_my_stats by focusing on resolved history and mistakes.

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 clearly states when this tool is appropriate: for self-review of resolved predictions, requiring API key and returning only user data. It does not explicitly name alternatives, but the comparison to siblings is implied by the 'your own data' and 'data only' framing, and the context makes the intended use clear.

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