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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description only needs to add interpretive context. It does so by disclosing the output structure (forward returns, sample size, baseline) and adding a critical caveat about small samples, which goes beyond the structured annotations.

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?

Three sentences, front-loaded with the main purpose, then examples, then output details and a caveat. Every sentence carries meaningful information without redundancy or fluff.

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 read-only analytics tool with a single parameter, an output schema, and full schema coverage, this description is complete. It explains what the tool does, what it returns, and includes a practical caution, leaving no significant gaps for an AI agent to invoke it correctly.

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 schema already covers 100% of the single parameter (ticker) with a clear description. The tool description references 'a ticker' but does not add further semantic nuance beyond what the schema provides, so the baseline score of 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 tool provides 'Historical base rates for the technical events firing on a ticker today' and specifies exactly what it returns: forward returns at multiple horizons, sample size, and an unconditional baseline. This distinguishes it from sibling tools like get_technical_indicators (current values) and get_relative_strength (current performance).

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 implies usage context by stating it answers 'what usually happened after this' and warns 'small samples warrant caution,' which tells users when to rely on it. However, it does not explicitly name alternatives or state when not to use it, though the differentiation from current-indicator tools is inferable.

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