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

Annotations already cover read-only and non-destructive behavior. The description adds meaningful context: results are cross-company, sourced from a 'legacy-thematic' knowledge graph, and focus on the gap between analyst pressure and management response. It does not describe output format, but the annotation coverage lowers the burden here.

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 compact, front-loaded with the primary purpose, provides examples, and then routes to the relevant sibling. Every sentence earns its place; no redundancy or filler.

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 no-output-schema, all-optional-parameter tool, the description explains what the tool is for, what kind of signals it surfaces, and how to distinguish it from related tools. The only gap is that it does not explicitly state the format of the returned divergence data, but the conceptual output is described well enough for initial selection and invocation.

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%, and the schema already provides parameter examples for 'search', 'sector', and 'industry'. The description adds no unique parameter-level details, so it aligns with the baseline for high schema coverage.

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 explicitly states the tool's function: cross-company analyst-management divergence detection from the knowledge graph. It adds concrete conceptual detail (executives deflecting, reframing, avoiding topics) and distinguishes itself from the sibling get_company_earnings, making it unambiguous among the sibling set.

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 gives explicit use-case phrasings ('where are executives deflecting?' or 'divergence in [sector] earnings') and explicit when-not-to-use guidance with an alternative tool: 'For per-ticker deflection signals, use get_company_earnings with view=qa and filter by response_directness.' This is the strongest form of usage guidance.

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