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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, indicating safe, non-destructive, repeatable behavior. The description adds value by detailing how off-calendar fiscal years are handled, the inclusion of citation URIs, and efficiency gains (replaces 8-15 sequential lookups). No contradictions.

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 moderately lengthy but front-loaded with natural-language triggers. Every sentence adds meaningful context (data sources, sorting, efficiency). A slight reduction in redundant phrasing (e.g., 'side-by-side... in ONE parallel call' could be tightened) would improve conciseness.

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?

Despite lacking an output schema, the description fully covers return format (paired data + citation URIs, sorted by primary metric) and data provenance. It addresses behavioral nuances like fiscal-year handling and explicitly states the tool's efficiency advantage, making it fully self-contained for an AI agent.

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%. The description reinforces parameter meaning: for 'type' it specifies data sources (SEC EDGAR/XBRL for company, FAERS/FDA/clinicaltrials.gov for drug); for 'values' it clarifies required format (tickers/CIKs vs. drug names) and the 2-5 item limit. This adds real context beyond the raw schema.

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 begins with concrete example queries like 'Compare X and Y' and 'rank these companies,' directly stating the tool is for side-by-side comparison of 2-5 companies or drugs. It clearly differentiates from siblings such as entity_profile by emphasizing parallel evaluation over sequential lookups.

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 includes an explicit directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies when to use each data type and notes that results are sorted by primary metric, guiding the agent on how to interpret output for queries like 'largest.'

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