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

Annotations already establish that this is a read-only, idempotent, non-destructive, open-world query. The description adds useful context about the attribution categories being reported, but it does not describe return structure, aggregation behavior, or data freshness beyond what the annotations provide.

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 definition is short and front-loads the core concept, with no filler. The second sentence duplicates the schema's window enum, which is mildly redundant, but overall it remains efficient and easy to parse.

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 simple one-parameter read-only tool, the description gives enough context about what attribution categories will be reported and what windows are valid. It does not explain the exact output shape, but the tool's complexity is low and the annotations cover the safety profile.

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 single window parameter has 100% schema description coverage, including its allowed values and default. The description repeats the accepted window values but adds no additional meaning beyond the schema, so the baseline 3 is appropriate.

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 clearly states the resource: mining-pool attribution provenance broken down into proven, claimed, inferred, or unattributed shares over a window. It is specific enough to distinguish this from pool distribution data, though it does not explicitly name or contrast sibling tools.

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 no explicit when-to-use or when-not-to-use guidance, and no alternative tools such as pool_distribution or pool_sources are referenced. An agent can infer that this is for attribution-confidence analysis, but the description leaves selection entirely implicit.

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