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

Beyond the readOnly/idempotent annotations, the description adds a significant interpretive trait: the numeric score has no predictive relationship with returns (r≈-0.02 in a 455-call backtest) and must be presented as 'how they communicated, never as a trading signal'. It also tells the agent that the qualitative payload is the useful part. This is substantive behavioral context not inferable from the 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?

Despite being longer than average, the description is tightly organized into purposeful sections: purpose, when-to-use, what-the-score-means, and data provided. Every sentence contributes—the example queries are illustrative, the backtest caveat is important, and the alternative-tool pointer saves agents from misrouting. The main purpose is front-loaded in the opening sentence.

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?

With no output schema, the description appropriately details the two return views (leaderboard and specific ticker) including the fields provided (score, classification, dodged questions, topic breakdown, historical trend). It also includes the crucial interpretation caveat and points to the sibling tool for deeper analysis. Nothing essential for correct use is missing.

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?

Schema description coverage is 100%: both 'ticker' and 'context' already have explanatory descriptions, including the optionality mapping to the leaderboard when ticker is omitted. The description adds example tickers and query phrasings that slightly reinforce usage, but does not materially surpass what the schema already conveys. 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 opens with a specific verb and resource ('Get Evade-o-Meter analysis for public companies') and immediately enumerates the core outputs: how directly management answered, which questions were dodged, and topic breakdown. It explicitly distinguishes itself from the sibling perception_get_earnings_intelligence by positioning that tool as the validated analytical lens, making the resource boundary clear.

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?

A dedicated 'WHEN TO USE' section lists concrete user queries for both the leaderboard and specific-ticker modes. It also gives an explicit exclusion: 'For the validated analytical lens on earnings calls, use perception_get_earnings_intelligence' and warns to never present the score as a trading signal. This provides both when-to-use and when-not-to-use guidance, plus a named alternative.

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