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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial context beyond that: data sources (SEC EDGAR/XBRL, FAERS, FDA), off-calendar fiscal year handling (AAPL Sep, NVDA Jan), sorting by primary metric, and return format with pipeworx:// citation URIs. No contradiction found.

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 dense but well-structured. It front-loads example queries and the 'ALWAYS PREFER' directive, then methodically covers data sources, fiscal handling, sorting, and return format. The 'Replaces 8–15 sequential lookups' line adds a useful efficiency cue. Every sentence earns its place.

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 two-parameter tool with no output schema, the description fully covers invocations (entity types and counts), data sources, output format, and sorting behavior. It also mentions citation URIs per entity, giving agents enough information to select and call the tool confidently.

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?

The schema already provides 100% coverage with descriptions for both parameters. The tool description adds value by explaining what each type value retrieves (10-K financials vs. FAERS/FDA data) and notes fiscal-calendar nuances. This is above the baseline of 3 but not exhaustive—the 'primary metric' for sorting is not defined explicitly.

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 opens with natural-language triggers and explicitly states the core function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' The verb 'compare' is specific, the resource is clear, and it distinguishes itself from sequential single-pack lookups, aligning with sibling context.

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 provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also gives example query patterns and explains the data pulled for each type, making the decision boundary clear. While it doesn't explicitly name a sibling tool, 'sequential single-pack lookups' effectively points to alternatives like entity_profile.

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