prediction_market_estimate

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

With no annotations, the description carries the full burden. It discloses that the estimate is experimental, that historical performance claims are unverified, what the return includes, and the cost mechanism ($0.05 x402 USDC on Base). This is strong behavioral context. It doesn't state failure modes, rate limits, or whether the micropayment is per-call or per-success, leaving minor gaps.

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?

Four sentences, front-loaded with the core purpose, then the experimental caveat, then the return values, then the cost. No wasted words; each sentence adds value.

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 tool with no output schema and no annotations, the description covers purpose, caveats, return fields, and cost. It is nearly complete. The only minor gap is the absence of guidance on when to use this versus siblings like prediction_market_ensemble, but overall it provides enough for an agent to call correctly.

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 coverage is 100%, so all parameters (question, market_price, context, category) are already documented in the schema. The description adds no additional parameter semantics beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 clear verb+resource: it produces a probability estimate for a prediction market question. It distinguishes itself from siblings like prediction_market_ensemble or prediction_market_size by being a single-model estimate, though it doesn't explicitly name an alternative. Purpose is unambiguous.

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 estimating probabilities on prediction market questions, but it doesn't say when to choose this over prediction_market_ensemble or prediction_market_scan. No explicit when/when-not guidance or prerequisites are stated. Usage is inferred from the name and description.

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