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

With no annotations, the description carries the behavioral burden. It provides useful behavioral context: coverage is a rolling 30-day window over matured forecasts, and the data is claimed to be non-curatable because predictions were committed before outcomes existed. It does not explicitly state that the call is read-only or whether any access restrictions apply, but 'public accuracy record' and the get-style name make the safety profile reasonably clear.

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 compact and front-loaded with the core message: this is the oracle's accuracy record. The second sentence is dense and contains statistical detail, but it still earns its place by explaining the scoring scope and integrity guarantee. Minor jargon like 'souls' and 'blind slab study' slightly reduce clarity.

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 is largely sufficient: it explains what the scorecard contains and why it should be trusted. It could be more complete by explicitly differentiating from sibling tools, especially get_graded_proof, but the agent can still invoke and interpret the tool correctly.

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 has zero parameters, so the baseline is 4. The description does not need to explain parameter meanings, and its content about coverage and provenance adds contextual rather than semantic value.

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 identifies a clear resource: the oracle's public accuracy record, and it specifies what it contains (rolling 30-day coverage, souls' scored track record, blind slab study). The purpose is implied through 'verify before trusting,' but there is no explicit action verb like 'returns' or 'retrieves.' It distinguishes from siblings like get_forecast and get_graded_proof conceptually, though not by name.

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 phrase 'verify before trusting' gives an explicit use case: call this before relying on oracle predictions. It does not, however, mention alternatives or state when not to use it, such as pointing users to get_graded_proof for individual prediction evidence or get_forecast for current forecasts.

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