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

The description discloses the composite nature of the score, the interpretation that higher scores mean more opaque, and the weekly update frequency. It also lists the returned fields. Annotations already mark the tool as read-only, and the description adds behavioral context without contradicting the 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 a single, dense sentence that front-loads the core return type and fields. It packs in usage guidance and update frequency without excessive verbosity, though the multiple clauses make it slightly less scannable than ideal.

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 lack of an output schema, the description compensates by naming the return fields and score interpretation. The parameter behavior is fully specified in the schema, and the annotations cover safety. Overall, the tool is well-contextualized for an agent to invoke correctly.

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 covers the single 'days' parameter completely, including its optionality, range, and Gold-tier requirement. The description does not add any parameter-specific semantics, so the baseline of 3 applies.

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 returns a 0-100 ownership-complexity score, lists the specific output fields (complexity_score, ownership_depth, relationship_count), and explains the data source (GLEIF LEI relationship graph). This is a specific verb+resource with sufficient detail to distinguish it from generic sibling tools.

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 use cases: counterparty legal-entity structure, beneficial ownership opacity, shell-company risk, KYC/AML due diligence, corporate ownership chains, and onboarding/sanctions-review timing. It does not name specific alternative tools or exclusion criteria, but the 'Call when' phrasing gives clear context.

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