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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds value beyond that by disclosing the content scope of the read (timeline, tone, forward-looking statement counts, YoY shifts), giving the agent a sense of what it retrieves.

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?

Two sentences, front-loaded with the core purpose before the signal enumeration and use cases. The middle list is long but each item carries distinct meaning, so little is wasted.

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 single-parameter read tool with an output schema and full annotation coverage, the definition is nearly complete. The output schema handles return formatting, so the description's enumeration is surplus rather than a gap.

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?

With a single parameter and 100% schema description coverage, the schema already documents the NZX ticker format and examples. The description adds no parameter-level detail, so the baseline 3 applies.

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?

States a specific verb and resource: tracking how a company's management narrative evolves over time, plus an enumeration of the underlying signals (tone, guidance, topics, hedging, LIS). This distinguishes it well from generic siblings like get_management_credibility or get_earnings, though it never names an alternative directly.

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 final sentence gives implied use cases (detecting narrative pivots, credibility assessment, earnings quality analysis), which is genuine context. However there is no explicit when-to-use vs when-not-to-use guidance or routing to competing tools such as get_transcript_diffs or analyse_narrative_risk.

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