Zovo LEI Lookup

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

Annotations already declare readOnly, openWorld, idempotent and non-destructive, so the safety profile is covered; the description adds real value on top: live public-index sourcing, XLSX/CSV output files, five candidates max per name, and that ambiguous matches require user choice. It doesn't mention rate limits or auth, but nothing critical is missing for a read-only lookup.

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?

Front-loads the trigger condition, then the input limits, then the exclusion — all in three compact sentences with no filler or repetition.

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 an output schema exists and annotations cover safety, the description supplies the remaining operational details (input caps, candidate count, ambiguity handling, output format). Only the sibling-alternative mapping is left implicit, which keeps it just under complete.

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% and both the name-length (2–200) and 25-name limits are already stated in the schema descriptions, so the description largely restates structured data. The five-candidates-per-name note adds slight context but is a result property rather than a parameter semantic.

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?

States a specific verb+resource: finding LEIs by company name, with an explicit synonym set (lei search, legal entity identifier lookup). It distinguishes itself from verify_leis only indirectly via the 'not for ownership verification' line rather than by naming siblings, so it falls short of full differentiation.

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?

Gives a clear trigger condition ('when the user wants to find lei by company name...') and an explicit exclusion ('Do not use for ownership verification'), which routes agents away from get_lei_parents/verify_leis. It never names the alternative tools, so agents must infer which sibling to use instead.

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

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond that: live public-index data, XLSX/CSV file output, and a 10-LEI cap with direct/ultimate parent or reporting-exception results.

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 front-loaded with the usage trigger and then adds scope, output behavior, limits, and exclusions in three tight sentences. The opening keyword phrase ('lei parent company, lei lookup, or legal entity identifier lookup') is slightly repetitive but still serves discoverability.

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?

With annotations covering safety, a complete parameter schema, and an output schema, the description supplies the remaining important context: data source, file format, lookup limits, and exclusions. It would be fully complete if it pointed to the appropriate alternative for ownership-chart requests.

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 description coverage is 100%, so the sole 'leis' parameter is already well documented with an example and the 10-LEI limit. The description restates the 10-LEI cap and adds parent-result context, but does not add syntax or format details beyond the schema.

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 states a specific operation: retrieving parent-company data for up to 10 LEIs from public-index records. It distinguishes scope from ownership charts and beneficial ownership, but does not explicitly name siblings such as find_leis_by_name or verify_leis.

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 gives a clear trigger (when the user wants an LEI parent company or legal entity identifier lookup) and an explicit exclusion (not for full ownership charts or beneficial ownership determination). It stops short of naming which alternative tool to use instead.

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

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

Beyond the annotations, it discloses meaningful behavior: reads live public-index data, returns dated XLSX/CSV files, handles up to 100 LEIs, and keeps invalid and duplicate rows in the report. These details materially help an agent understand the operation and its output.

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 front-loaded and reasonably short, but the first sentence is repetitive and keyword-heavy with repeated 'lei lookup,' 'lei check,' and 'verify lei numbers.' It could be tightened without losing meaning.

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?

With rich annotations, an output schema, and full schema coverage, the description provides enough context to call the tool correctly. It covers purpose, trigger conditions, output format, limits, renewal-date behavior, and invalid/duplicate handling.

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 description coverage is 100%, so the input schema already documents both the LEIs array and the optional renewal date. The description reinforces the 100-LEI limit and optional renewal date but adds no syntax or format detail beyond the schema.

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 states a specific verb and resource: verify LEI numbers, check renewal dates, and detect lapsed LEIs. It is distinguishable from find_leis_by_name and get_lei_parents by focusing on verification of supplied LEIs, but it does not explicitly name those siblings or clarify the boundary against a generic LEI lookup.

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 gives a clear trigger: use when the user wants an LEI lookup, check, or renewal-date verification. It also includes an explicit exclusion against legal or regulatory advice, though it does not name alternatives such as find_leis_by_name for name-based lookup.

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