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

The annotation only provides readOnlyHint, so the description adds meaningful transparency with 'Precomputed by the backtest — no model call, no vendor call' and 'not a forecast.' It discloses the nature of the data (distribution over real occurrences) without contradicting annotations. It could mention data freshness or edge cases, but the key behavioral traits are covered.

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 dense and mostly efficient, front-loading the main concept and including useful examples of event types. Some redundancy exists between 'what HISTORICALLY FOLLOWED' and 'A what happened in situations like this record,' but overall every major idea is purposeful and the structure is readable.

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-only tool with an output schema, the description is nearly complete: it explains what is returned, that it is historical rather than predictive, and how to use it. It could optionally mention absence of company-specific filtering or data coverage, but these are not critical given the schema and annotations.

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?

There are zero parameters and the schema already covers them trivially, so the 0-parameter baseline of 4 applies. The description does not need to add parameter-level meaning because no parameters exist, and it appropriately focuses on output semantics instead.

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 defines the tool as a historical event-studies record: for each thesis-breaking event type, it provides counts, distinct companies, and forward excess returns. It also distinguishes itself from a forecast and clarifies it is precomputed, separating it from siblings like get_recent_breaks or get_thesis_impact.

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?

The description explicitly states when to use it: 'Use to build or answer the fundamentals event-studies view.' It also signals that it is a historical record, not a forecast, which helps avoid misuse. However, it does not explicitly name alternative tools or state when not to use it, stopping short of a 5.

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