predictions_search

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

With no annotations, the description carries the full behavioral burden and does well by disclosing the cost ($0.01 USDC per call), payment rail (x402 on Base), and automatic wallet deduction. It does not describe rate limits, failure modes, or result ordering, which keeps it from a 5.

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?

Two sentences, front-loaded with the core purpose and followed by the critical payment detail. Every clause earns its place, and no filler is present.

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 simple two-parameter search tool with no output schema and no annotations, the description supplies purpose, sources, and payment behavior. It could say more about the shape of returned probabilities, but the essential call-time context is present.

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 description coverage is 100%, so the schema already documents both q and limit with examples. The description reinforces that q is keyword-based but adds no format, syntax, or usage constraints beyond what the schema provides.

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?

Uses a specific verb ('Search') and resource ('prediction markets'), and explicitly scopes it to keyword lookup of market-implied probabilities across Polymarket and Kalshi. This distinguishes it from siblings like predictions_top and predictions_movers, which do not take a keyword.

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 keyword-based lookup and names example domains, but it does not explicitly state when to use this versus predictions_top, predictions_movers, or macro_series. No exclusions or alternative-routing guidance are provided.

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