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

Even though annotations already declare readOnlyHint=true and openWorldHint=true, the description adds substantial behavioral context: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), explains how off-calendar fiscal years are handled, notes that results are sorted by primary metric, and mentions the return format (paired data + pipeworx:// citation URIs). This goes far beyond what annotations cover, giving the agent a clear model of the tool's internal behavior.

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 front-loaded with trigger phrases and the core purpose. It uses dashes and clear sections to separate trigger examples, data source details, and output format. While slightly verbose, every sentence adds value and the structure aids comprehension. A 4 is warranted because it's somewhat long but not bloated.

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?

Considering there is no output schema, the description compensates by explicitly mentioning the return format (paired data + citation URIs), the data sources per type, fiscal year handling, and the benefit of replacing sequential lookups. It covers all essential aspects an agent needs to know to select and invoke the tool correctly, making it highly complete without an output schema.

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%, but the description adds significant meaning by explaining what each type ('company' vs 'drug') pulls: company data includes 'LATEST 10-K revenue + net income + cash + long-term debt,' while drug data includes 'FAERS adverse-event counts, FDA approval counts, active trial counts.' It also clarifies that values are tickers/CIKs or drug names, and that results are ordered by primary metric, far exceeding the schema's basic enum and array definition.

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 a specific verb ('compare') and resource ('entities'), and explicitly defines the action as 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It distinguishes itself from sibling tools like entity_profile by emphasizing the parallel comparison aspect and listing concrete trigger phrases.

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' and states 'Replaces 8–15 sequential lookups.' It also enumerates example queries ('Compare X and Y', 'rank these companies', 'head to head') that signal when to invoke this tool, making the decision boundaries clear.

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