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

Annotations already declare readOnlyHint, destructiveHint, and idempotentHint. The description adds useful behavioral context: the output format (list of companies with values, top examples), period format specifics, and the tool's scale (2,649 companies). This goes beyond the structured 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 three well-structured sentences, front-loaded with purpose, followed by an illustrative example and a note on period format. Every sentence adds value with no repetition or fluff.

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?

Given the presence of an output schema and clear annotations, the description covers purpose, usage, examples, and period format. No critical information is missing; it is complete for the tool's complexity.

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%, baseline 3. The description adds examples for tag and period (e.g., 'Revenues', 'CY2023', 'CY2023Q4I') and clarifies default unit ('USD'). This enhances understanding beyond the schema descriptions.

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 states the tool compares a financial metric across all SEC-reporting companies for a period, with a specific example (Revenues for CY2023) and output highlights. The title 'Cross-Company Financial Comparison' distinguishes it from sibling tools like finance.edgar.company_facts (single company).

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 clear context for when to use (broad market comparison) and contrasts with alternatives ('Free alternative to Bloomberg/FactSet'). However, it does not explicitly exclude usage for single-company queries or compare to other Edgar siblings like xbrl_concept.

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