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

Despite annotations already indicating readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds valuable behavioral context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), mentions specific return fields, and discloses limitations like the USPTO API sunset soft-failure and GDELT→GNews fallback. No contradictions with 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 somewhat long (multiple sentences) but each sentence adds value: example queries, explicit usage preference, list of data sources and return fields, and constraint notes. It is front-loaded with examples and avoids redundancy. Could be slightly more concise but is well-structured for the complexity.

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 tool's complexity (multiple data sources, potential failures, and no output schema), the description is remarkably complete. It covers edge cases (name unsupported, patent API sunset soft-fail, news fallback), lists all return fields with details (e.g., pipeworx:// URIs for filings), and directs to resolve_entity for missing functionality. No gaps for an agent to guess.

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%, with both parameters already described in the JSON schema. The description adds context beyond schema: explains that 'type' currently only supports 'company' (others coming soon), and 'value' must be ticker or zero-padded CIK, not names. It also links usage to resolve_entity for names, adding practical guidance.

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's purpose as 'full cross-source profile of a US public company in ONE parallel call' with concrete example queries (e.g., 'tell me about X', 'research Acme'). It differentiates from sibling tools like resolve_entity for name resolution and compare_entities for 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?

Explicitly advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies input constraints: 'Pass ticker "AAPL" or zero-padded CIK... names not supported (use resolve_entity first if you only have a name).' This provides clear when-to-use and when-not-to-use guidance.

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