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

Annotations already cover read-only, idempotent, and closed-world behavior, yet the description adds real substance: prices are captured while markets are OPEN and graded as they mature so the grade cannot be back-filled, and no proprietary forecaster is published. That is meaningful behavioral context beyond the structured fields.

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 lead is informative but the paragraph is dense, em-dash-heavy, and repeats its hedging ('here or in the full dataset', 'we sell a maintained, source-traced record') in ways that delay the point. It is not wasteful enough to be poor, but it is far from tight, front-loaded prose.

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 parameters, the description carries the burden of describing the return payload, and it does so — Brier, log loss, skill vs base rate, 95% CIs. It is close to complete for a no-arg read tool, missing only format/shape details an agent might want.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; per the baseline for 0-param tools this scores 4. The description correctly implies a parameterless retrieval and adds no misleading input claims.

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 states a specific resource — the grading dataset for the market sensor — and enumerates its contents (crowd price, base_rate null, Brier score, log loss, skill, 95% CIs). An agent can tell what it retrieves, though it never names a sibling tool to differentiate itself from get_signals or todays_brief.

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?

It implies context through 'we serve current state, not forecasts' and points to a fuller alternative endpoint (GET /api/calibration-data), which is some routing guidance. But there is no explicit when-to-use/when-not statement and no reference to any sibling tool, so selection still requires inference.

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