chainvectorPredictionsResults

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It implies a read-only historical data operation but never explicitly states read-only status, side-effect freedom, pagination, rate limits, or authentication requirements. It does not contradict annotations (there are none), but it adds no behavioral context beyond the purpose.

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 a single sentence with a clear subject-action-object structure and a clarifying em-dash parenthetical. No redundant words or unsupported claims; it front-loads the main purpose without requiring multiple reads.

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 should explain what the tool returns. It provides conceptual guidance ('calibration ground truth') but does not specify the response structure, fields, or pagination behavior. The schema documents parameters well, but given no output schema and no annotations, the description leaves some ambiguity about the exact data shape. For a relatively simple lookup tool, this is a moderate gap.

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 provides 100% parameter coverage with descriptions for limit, venue, and series. The description adds slight context by mentioning 'a series' and the notion of price-based calibration, but it does not explain limit defaults or venue semantics beyond what the schema already provides. Therefore, a baseline score of 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?

The description clearly states the tool returns settled prediction-market outcomes for a given series, explicitly framing it as calibration ground truth. This distinguishes it from sibling tools like chainvectorPredictionsQuotes (quotes) and chainvectorPredictionsMarkets (markets) by focusing on realized outcomes. The verb 'settled' acts as a specific retrieval action, and the resource is well-defined.

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 the tool is for calibration analysis ('how often did YES actually win at each price?') but does not explicitly state when to prefer it over alternatives or exclude it from other scenarios. No sibling comparisons or conditional guidance is provided, leaving usage context largely implicit.

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