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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond those annotations: the data is keyless/CC0 open data, name searches are fuzzy and return ranked candidates, LEI lookups are exact, and the limit is ignored for exact LEI matches. It also lists the specific fields returned, giving the agent a clear behavioral contract.

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 dense but every sentence earns its place: what the tool does, the data source and licensing, returned fields, downstream use cases, and input behavior. It is front-loaded with the core action and avoids filler.

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?

There is no output schema, so the description carries the full burden of explaining return values. It enumerates the fields returned (LEI, legal name, status, jurisdiction, legal-form code, address, registration status, last-update date) and fully covers the input modes. The description is complete enough for an agent to select and call the tool correctly without further inference.

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 both parameters thoroughly, so the baseline is 3. The description adds extra semantic value by clarifying that name search is fuzzy and returns ranked candidates, while LEI input is exact. This goes slightly beyond the schema, which merely labels the query as a name or LEI code.

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 specific verb and resource: 'Resolve a company name to its Legal Entity Identifier (LEI), or look up an LEI code directly.' It also names the data source (GLEIF), making the operation concrete and clearly distinguishable from nearby sibling tools like lei_hierarchy or entity_resolve.

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 clearly explains when to use the tool: for entity disambiguation and cross-referencing against sanctions, SEC EDGAR, federal spending, and due-diligence tools. It also specifies the input forms (name vs. LEI). However, it does not explicitly state when not to use it or name alternatives, so it falls short of a 5.

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