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

No annotations are provided, so the description carries the full disclosure burden. It does explain the underlying model, live/armed match scope, leagues, output fields, and auto-fire threshold, but it omits freshness, rate limits, auth requirements, and whether the tool is strictly read-only.

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 description is front-loaded and dense, but it is truncated mid-phrase ('in-play predictio') and includes marketing-style language about losses that does not help an agent select or invoke the tool.

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 no-parameter data-retrieval tool with no output schema and no annotations, the description covers the core signals and leagues but does not fully specify return format, data freshness, or coverage limits, and the truncated ending leaves the usage statement incomplete.

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 has zero parameters, so there are no parameter semantics to clarify. Per the rubric, a zero-parameter tool receives a baseline of 4 when the schema coverage is complete.

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 resource and data type: Polymarket in-play soccer betting edge signals, with leagues, model-vs-market comparison, edge percent, ask price, and the 12% auto-fire bar. It is clearly distinct from the unrelated sibling tools, though it does not explicitly differentiate itself from them.

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 provides explicit use cases: 'Use for Polymarket edge hunting, sports betting model predictions, and in-play predictio[n]...' This gives clear context for when to invoke it, but it does not name exclusions, alternatives, or prerequisites.

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