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

Annotations already declare read-only, idempotent, non-destructive behavior, so the description need not repeat that. It adds non-obvious behavior by describing coverage of 517 consumer-sector companies and the web-backfill fallback path. No contradiction with 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?

The description is front-loaded with the per-ticker purpose, then gives useful content expectations, then states fallback behavior and sibling routing. Every sentence carries operational value and there is no filler or vague marketing language.

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 tool with a fully documented 9-parameter schema, the description provides a strong mental model of what the tool returns and what it can do for covered vs. uncovered tickers. The lack of a detailed return schema means the description cannot fully specify output shape, but it names the major record components clearly.

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?

Parameter schema coverage is 100%, so the description is not required to explain each parameter. It adds some output-oriented context, such as what a record contains, but it does not add semantic meaning for view, period, mode, tickers, or sector beyond what the schema already gives. 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 clearly identifies this as the canonical per-ticker earnings source and states its specific contents: analyst concerns, sentiment labels, strategic activity, CEO intelligence, and validated consumer trends. It also differentiates the tool from get_earnings_intelligence, which is the key sibling ambiguity.

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?

It explicitly routes usage: 'Use this for company-specific data' and 'Use get_earnings_intelligence for cross-company thematic comparisons.' It also tells the agent that uncovered tickers fall back to web-backfill, which helps an agent decide whether this tool can handle an out-of-coverage query.

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