get_opportunities

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It explains that the tool returns divergence opportunities ranked by score and that higher scores indicate greater disagreement. It does not discuss authorization, rate limits, or destructive effects (not applicable). The description is transparent enough for a read operation, though it could mention if results are cached or if there are any known limitations.

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 concise and well-structured. The purpose is stated in the first sentence, followed by a one-sentence explanation of divergence. The Args section clearly lists each parameter with its description, and the Returns section specifies the output format. Every sentence contributes meaning without redundancy.

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 that the tool has an output schema (assumed structured), the description still explains the return format ('Formatted list of divergence opportunities ranked by score'). All three parameters are documented with defaults and valid values. Similarly, the sibling tools are listed for context (though not compared). The description is complete for a list-returning tool with no required parameters.

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% description coverage, meaning the schema provides no documentation for parameters. However, the tool description fully compensates by listing each argument with its meaning, default values, and valid options (e.g., sport: 'NBA, NFL, MLB, NHL, MLS, POLITICS, or omit'; limit: '1–20, default 10'). This adds significant value beyond the bare schema.

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 opens with a specific verb and resource: 'Get events where prediction markets show notable divergence.' It defines divergence opportunities concisely and distinguishes this from sibling tools by focusing on divergence and disagreement, which is unique among get_consensus, get_markets, get_settlements, and get_signals.

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: it surfaces events with notable disagreements, possibly where information is still being incorporated. This implies when to use it (when seeking mispricings or inefficient markets), but it does not explicitly state alternatives or when not to use it. The sibling tool names are listed, but no direct comparison is made.

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