prediction_markets

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

No annotations are provided, so the description carries the full burden. It goes beyond a simple listing by explaining that 'Prices are probabilities: 0.63 means the market prices that outcome at 63%', which is a key behavioral trait for interpreting results. It does not discuss rate limits or permission requirements, but for a read-only list operation, the disclosure is reasonably sufficient.

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. It leads with a single-sentence purpose, then uses an Args block for parameters, and adds a brief critical note about probability interpretation. Every sentence earns its place; no fluff or repetition.

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 an output schema exists, the description does not need to explain return values. It covers the tool's purpose, parameters, and the interpretation of the price field. For a list operation, this is complete. The only minor omission is explicit discourse on alternatives, but that is not required for completeness here.

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 only titles and defaults with no descriptions (0% coverage). The description fully compensates by explaining each parameter: limit (max 100), order (valid options), and tag (optional category slug with examples). This adds essential meaning beyond the 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 'List the most active Polymarket prediction markets', which clearly specifies the verb (list), resource (Polymarket prediction markets), and scope (most active). This differentiates it from sibling tools like prediction_search (which searches) and prediction_market_detail (which retrieves details).

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 clearly indicates when to use this tool: to list the most active prediction markets. It provides parameter details (limit, order, tag) that help tailor the list. However, it does not explicitly mention alternatives or exclude cases (e.g., when searching for a specific market), so it misses the top score for explicit when-not guidance.

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