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Glama

cuisor.ro

get_legal

Court dockets (portal.just.ro) and BPI publications for one company, by CUI. Covers roughly the last three years, not a complete litigation history — it answers whether a company is in court now or recently, not whether it has ever been sued. Includes ambiguous matches the public website never shows: a docket whose party name is shared by more than one company in the register, fanned out one row per candidate CUI. No co-party names, no outcome or merits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cuiYes

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Explicitly discloses that ambiguous matches are included and fanned out per candidate CUI, and lists exclusions (no co-party names, no outcome or merits). This fully informs the agent about what the result will and will not contain, exceeding typical expectations.

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?

Description is dense but every sentence adds value: sources, scope, ambiguity handling, exclusions. It is efficient without being bloated, conveying necessary detail in a compact form.

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

Completeness5/5

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

Despite having no output schema, the description adequately explains the nature of the data (court dockets, BPI publications), the structure (one row per candidate), and limitations (date range, absent fields). This gives the agent sufficient context to interpret results correctly.

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?

Only one parameter (cui) exists and the tool description refers to it as 'by CUI', but does not define what CUI stands for or the expected format. Schema provides no description either, leaving the parameter meaning partially inferred 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?

Clearly states it provides court dockets and BPI publications for a single company identified by CUI, using specific sources (portal.just.ro). The verb is implied but the resource and scope are unambiguous, and it differentiates from sibling tools that focus on other company data types.

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?

Provides clear scope and limitations: covers last three years, not a complete history, and defines what it answers vs. what it doesn't. While it doesn't explicitly name alternative tools, the description itself offers enough context to decide when to use this tool for legal-focused queries.

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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