get_earnings_divergence

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is partially known. The description adds that the tool performs reads from two Airtable tables and a sentiment gap calculation, which is useful operational context. However, it does not disclose whether the operation is expensive, whether it caches results, or what the idempotency=false means in practice.

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 two sentences, but the second sentence is a parenthetical list of internal steps that reads like implementation notes rather than front-loaded value. It is not overly wordy, but the structure could be improved by leading with the core output and keeping the technical details secondary.

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 tool with no output schema and 7 optional parameters, the description gives a high-level idea of what is computed but does not explain what the output looks like (e.g., themes, deflection counts, scores). It is adequate but leaves gaps that an agent would need to infer.

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 coverage is 100%, so all 7 parameters are fully described in the schema. The description adds no parameter-level detail beyond what is in the schema; it simply notes the data sources involved. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific analytical purpose ('comparing analyst line of questioning against executive management prepared remarks'), which is clearer than a tautology. However, it is dense with internal jargon ('Truth-layer analysis', 'divergence matrix') and does not clearly distinguish this tool from siblings like get_earnings_intelligence or get_company_earnings. An agent might struggle to know when exactly to pick this over those.

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 guidance on when to use this tool versus the many other earnings-related siblings (get_earnings_intelligence, get_company_earnings, get_my_earnings). The description implies a specific use case but offers no routing cues, prerequisites, or exclusions.

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