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

Annotations already cover read-only/idempotent/closed-world, so the description earns credit for adding real context: cursor pagination is id-ordered and bulk reads require following next_cursor, plus the caveat that these are paper-research forecasts rather than advice. It stops short of stating volume expectations or auth/quota constraints, which would push it to 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?

Front-loaded with Purpose and tightly labeled sections (Triggers, When to call, Prerequisites, Next steps, Caveats) that make scanning easy. It loses a point because the Output block enumerates every field of a response that already has an output schema, which is redundant text.

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?

Six optional parameters, an output schema, and rich annotations are all accounted for; the description supplies the audit/provenance framing, pagination handling, and the interpretive caveat about past hit rates. Nothing an agent needs in order to call this correctly is missing.

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% — day, limit, cursor, status, target_market, and source_category are all documented inline, including alias handling for target_market. The description adds only the id-ordered cursor mechanic and a partial field listing from the output, so baseline 3 applies.

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 verb and resource — a raw row-level prediction ledger covering each prediction's full lifecycle (created_at -> resolved_at -> outcome) — and explicitly positions it as the row-level evidence underneath get_prediction_accuracy's aggregates. An agent can distinguish it from the aggregate sibling without opening either schema.

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?

Gives explicit triggers (quoted user phrasings, including Korean), a 'When to call' list, a sequencing note (after get_prediction_accuracy), and a next step (get_ledger_integrity). The gap is the closest sibling: get_active_predictions overlaps heavily with status="open" here, and the description never tells the agent which to prefer.

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