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

No annotations exist, so the description carries the full load, and it discloses the single most important behavioral trait: the call costs $0.005 USDC on Base via x402. It also enumerates the returned fields. It does not clarify whether the charge is per call, what happens on failed/unmatched queries, or any rate limits, which keeps it below 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: the first front-loads what is returned, the second gives concrete query shapes, and the cost note is a short trailing clause. No filler and no repetition of the schema.

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?

With no output schema and no annotations, the description correctly compensates by listing the response fields and the payment requirement. It is nearly complete for a 3-parameter read tool; only failure/empty-result behavior and payment mechanics are unaddressed.

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%, with each of q, limit, and closed documented in the schema itself, so the baseline is 3. The description's 'for any topic' adds only loose framing for q and says nothing about limit or closed.

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?

States a specific resource and source ('Prediction market odds ... from Polymarket') plus the exact payload returned (outcomes, implied probabilities, 24h change, volume, liquidity, end date). This clearly separates it from sibling data tools like crypto_price or economy_indicators, which return spot values rather than market-implied probabilities.

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?

Concrete example queries ('odds of a Fed rate cut', 'will bitcoin reach 150k') make the intended use case unmistakable. It stops short of explicit exclusions, e.g. telling the agent to use crypto_price for a raw BTC quote, so there is no when-not-to-use guidance.

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