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

Annotations already mark the tool as read-only and non-destructive. The description goes further by disclosing the fan-out across multiple external APIs, the patents soft-fail due to the USPTO API sunset, the GDELT→GNews fallback mechanism, and the specific return structure. This level of behavioral detail exceeds what annotations provide.

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?

While the description is long, every sentence carries essential information: example queries, purpose, usage preference, data sources, return fields, and parameter format. It is structured as a single dense paragraph with semicolon-separated return details, earning its length.

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?

Without an output schema, the description is responsible for explaining what the tool returns. It enumerates all return components (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) and handles failure/fallback scenarios. This makes the tool 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?

The input schema has 100% description coverage for both parameters, including enum constraints and examples. The description repeats zero-padded CIK and the resolve_entity fallback, but these are already present in the schema, so no additional semantic enrichment is achieved.

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 multiple example user queries and states the tool creates 'a full cross-source profile of a US public company in ONE parallel call.' It clearly specifies the data sources and return fields, distinguishing it from sibling tools that focus on single aspects or comparisons.

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 instructs to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' when a holistic view is requested, and warns that names are not supported and to 'use resolve_entity first' when only a name is available. This provides clear when-to-use and when-not-to-use guidance relative to alternative tools.

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