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

Annotations already establish that this is a non-destructive, closed-world read-only operation. The description adds useful content context by naming sample sizes, baselines, measurement windows, and the public proof page provenance, but it does not describe freshness, response structure, or limitations.

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 a single front-loaded sentence with no filler. It states the return subject and key included fields immediately, so every clause contributes useful information.

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 parameterless read-only tool, the description gives the main return contents and provenance. However, without an output schema it does not specify the response shape, how baselines are interpreted, or the relationship to sibling proof tools, leaving some contextual gaps.

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 explain, and the baseline for a parameterless tool is 4. The description correctly adds no irrelevant parameter detail.

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 uses a specific verb ('Returns') and resource ('published track record') and names the included data (sample sizes, baselines, measurement windows). It is clear what the tool produces, but it does not explicitly distinguish it from proof-related siblings such as get_proof_summary or get_proof_anchors.

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 guidance, no exclusions, and no named alternative among the many sibling proof/trust tools. The reference to the public proof page implies a verification context, but the agent is not told when this is preferable to get_proof_summary or get_proof_anchors.

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