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NeuralVerge Business Intelligence

Latvia company

run_lv_companies

Get one company from the Latvia registry (Register of Enterprises) by registration number or registry URL: name, status, registration date, address, legal form, capital, officers, shareholders and beneficial owners. Flat 5 points per successful run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesRegistration number or registry URL. e.g. 40003000642

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the cost model ('Flat 5 points per successful run'), which is genuine behavioral context, and 'Get' implies a read operation. However, it omits auth/permission needs, error behavior for unknown registration numbers, and what happens on a miss.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One front-loaded sentence with no filler; the lookup key and returned fields come first and the cost note trails. The nine-field enumeration is long but earns its place given there is no output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description usefully compensates by listing the exact fields returned and the point cost. Remaining gaps are minor: failure behavior for invalid identifiers and any rate or coverage limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema already documents the single 'company' parameter with an example ('40003000642'). The description restates the same accepted formats without adding syntax, normalization, or validation detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Get one company from the Latvia registry'), names the source (Register of Enterprises), and enumerates the returned fields. 'One company ... by registration number or registry URL' implicitly separates it from the plural search sibling, but no sibling is named explicitly, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The identifier types ('registration number or registry URL') imply when the tool applies, but there is no explicit when-to-use guidance, no mention of run_lv_companies_search for name/query lookups, and no note on run_lv_company_filings for filing data. Usage must be inferred from the sibling list.

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

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