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

With no annotations, the description carries the full behavioral burden. It usefully discloses the validation methodology ('strict 90-day walk-forward cross-validation') and that a live sample count of realized 30-day outcomes is included, which adds real context. But it gives no indication of the response shape or what metrics constitute the 'track record', and no output schema exists to fill that gap.

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?

A single sentence, front-loaded with the primary return value and zero filler. The secondary clause about live filing counts earns its place because it distinguishes this from a pure historical stats endpoint.

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 zero-parameter read tool this is nearly adequate, but with no output schema and no annotations the agent still does not know what fields make up the track record (accuracy, precision, sample sizes, date ranges) or how the count relates to it. The description should say more about the return contents given nothing else documents them.

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 the baseline is 4. There is nothing parameter-level for the description to clarify or compensate for.

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 ('the model's validated track record') and specifies the methodology (strict 90-day walk-forward cross-validation) plus a secondary live filing count. This is clearly distinct from siblings like get_company or get_latest_filings, though it never explicitly contrasts itself with 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?

Usage is implied: an agent can infer this is the tool to call when it needs confidence in the model's predictions before acting on signals. However, there is no explicit when-to-use statement, no prerequisites, and no mention of alternatives for evaluating model quality.

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