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

With no annotations to rely on, the description carries the full burden and delivers rich detail: append-only receipts, simultaneous market price capture, deterministic scoring with Brier scores, inclusion of accuracy and methodology, and the explicit statement that losses are published and the record is auditable, not curated.

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 three well-structured sentences: the first states the core purpose, the second explains methodology and data integrity, and the third gives usage triggers. No wasted words; every sentence adds value.

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 zero-parameter tool with an output schema, the description sufficiently covers what the recipient will learn (Brier scores, accuracy, methodology, auditable data). It provides enough context for an agent to decide whether to invoke it and what to expect in the response.

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 tool takes zero parameters, so the description cannot add parameter-level semantics. Per rubric, 0 params earns a baseline of 4, and the description appropriately focuses on what the tool returns rather than inputs.

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 explicitly states the tool retrieves VoxOdds' audited AI-vs-market forecast track record, using a specific verb and resource. It also distinguishes from siblings by focusing on aggregate AI-vs-market performance rather than individual forecaster records (get_forecaster_record) or market odds.

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 includes explicit trigger scenarios: 'Call this when the user asks whether AI forecasts beat prediction markets, how reliable VoxOdds' AI is, or for citable forecasting-performance data.' It provides strong usage context, though it does not explicitly name alternatives or state when not to use it.

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