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

With no annotations, the description carries the full burden and does so well: it discloses that these are derived analytics only, that horizon prices are captured before resolution and never resolution-day, that data accrues durably, and that reliability deciles only appear once a cell reaches n ≥ 200. The pricing model (free via x402) is also stated. It stops short of 5 because nothing is said about what an empty or sparse result set looks like.

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 core purpose is front-loaded in the first sentence, which is good. But the paragraph is dense and repeats the free/paid framing twice ('Free:' and 'free so you can judge market prices before paying'), and the trailing bracket of HTTP route, price, and docs URL adds bulk. Information-dense but not tight.

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 needs to convey both content and behavior, and it largely does: it lists the metrics, the dimensions, the horizon semantics, and the sample threshold. What remains thin is how the venue/category inputs constrain the returned cells. For a derived-analytics read endpoint this is close to complete.

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 0%, so the description must compensate. It does add real meaning by framing the output as 'per venue × category × horizon', which tells the agent that venue and category act as filtering dimensions. However, it never maps the venue enum (kalshi, polymarket) or describes what a valid category string looks like, leaving part of the 2-param schema opaque.

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 names a specific verb+resource combination: a calibration scorecard that measures how well each prediction-market venue's prices forecast outcomes. It enumerates the concrete contents (Brier score, base rate, sharpness, sample counts, reliability deciles), so the agent knows exactly what kind of artifact this is. It falls short of a 5 only because it does not distinguish itself from the near-identical sibling probability_venue-calibration-detail.

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?

There is implied guidance: it is free, and it is meant to let you judge market prices before paying for anything built on them, which positions it as a low-cost first look. However, it never states when to use this tool versus probability_venue-calibration-detail or edge_calibration, and gives no explicit exclusions. Usage is inferable but not spelled out.

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