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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses the custom re-checking behavior: the register's token search returns thousands of unrelated rows, so only exact legal-form-equivalent matches are reported as found and others become candidates. It also adds pricing, auth, and free-tier details, giving the agent a clear picture of call behavior and requirements.

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 long but densely informative. The core purpose is front-loaded, followed by necessary behavioral details and pricing. Every sentence contributes useful operational information, though the pricing section could be considered slightly beyond the core selection criteria.

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?

For a single-parameter, read-only lookup tool with an output schema, the description covers everything an agent needs to select and invoke it: purpose, matching semantics, candidate vs found behavior, required input, pricing, and authentication requirements. No critical context is missing.

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?

With 100% schema coverage, the baseline is 3, but the description adds meaningful semantic context: the input 'name' should be the company legal name, and the matching logic tolerates only legal-form differences (Inc/Ltd/GmbH). This helps an agent understand what the parameter means and how results will be classified.

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 a clear question and then states the specific action: 'Look a company up in GLEIF, the global Legal Entity Identifier register', with a concrete list of outputs (LEI code, legal name, status, jurisdiction, address). This gives a clear verb, resource, and scope, though it does not explicitly distinguish itself from 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 implies when to use the tool ('Does this company legally exist, and where?') and explains the matching behavior, but it does not state when not to use it or name alternatives. Usage context is present but not explicit.

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