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Glama

cuisor.ro

get_company

Identity, status, address, activity and headline figures for one company by CUI. Slim by default. Pass include=['financials','procurement','change_history','peers','same_address'] for more. Carries as_of dates: the register and ANAF are refreshed on different schedules. Data: the Romanian company register (~4M companies) with filed financial statements, ANAF fiscal status, public procurement awards, and legal representatives. Amounts are in lei (RON). Not real-time: every payload states the age of the data it came from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cuiYes
includeNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the non-real-time nature, as_of dates, differing refresh schedules, RON currency, default slimness, and data provenance. This is substantial, honest behavioral context beyond what the schema shows.

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

Conciseness5/5

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

The description is compact and front-loaded: purpose first, expansion options second, then caveats and data context. Every sentence adds useful information, and there is no filler or redundant restatement of the tool name.

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?

For a two-parameter tool with no output schema and no annotations, this is quite complete: it covers purpose, fields, optional expansions, data source, currency, and data freshness. The main gaps are ambiguity around what exactly 'activity' and 'headline figures' include, and no mention of behavior for unknown or missing CUIs.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It explains cui as the company identifier and enumerates valid include values: financials, procurement, change_history, peers, same_address. It does not define CUI itself or expand on each include value, but the meaning is largely inferable from context.

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

Purpose5/5

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

The description names a specific resource ('one company by CUI') and the fields returned ('Identity, status, address, activity and headline figures'), with an implied fetch verb. It differentiates from sibling tools like get_financials or get_procurement by presenting an aggregate one-company profile and listing expandable sections.

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

Usage Guidelines4/5

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

It clearly states the core use case: retrieving one company by CUI, with a slim default and optional include expansions. It does not explicitly name alternatives like search_companies or list_companies or explain when to prefer them, but the one-company-by-CUI framing is clear enough for selection.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct domains: company identity, financials, legal, officers, procurement, stats, and CAEN lookup. The only ambiguity comes from the redundant connector aliases (fetch vs get_company, search vs search_companies), but their descriptions explicitly call out the duplication and direct agents to the canonical tools.

Naming Consistency4/5

The set mostly follows a clear verb_noun convention: get_company, get_financials, get_officers, list_companies, search_companies, resolve_caen. Minor deviations are company_stats (noun_verb) and the bare connector aliases fetch and search, but all names are lowercase and underscore-separated, so the pattern remains predictable.

Tool Count5/5

With 12 tools, the surface is well-scoped for a company information/registry API. Each tool covers a meaningful slice of the domain—search, company details, financials, legal, officers, procurement, stats, metadata, and CAEN resolution—without redundant or trivial additions beyond the two explicitly labeled connector aliases.

Completeness5/5

For a read-only company data API, the coverage is thorough: name/CUI resolution, company identity with optional includes, full financial history, legal records, officers, procurement, and aggregate statistics are all present. There are no obvious dead ends; even data freshness and completeness are addressed by get_meta.

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