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cuisor.ro

by io.github.idragulin

search_companies

Resolve a company name or CUI to candidate companies. Use this first whenever the user names a company rather than giving its CUI. Returns at most 50 candidates, active companies first, largest turnover first. Romanian company names collide heavily, so show the user the candidates rather than assuming the first is right. An unquoted query matches every name containing the words (mina srl: 1015 companies); wrap a name you already know in double quotes to get only the companies called that ("mina srl": 14). Case, diacritics and SRL/S.R.L. all fold. Quote a name, not a CUI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description fully discloses matching semantics (unquoted vs quoted, word matching, case/diacritics/SRL folding), result ordering (active first, largest turnover first), and the limit of 50 candidates. It also warns that name collisions are common, so the agent should show candidates to the user. No side effects are mentioned, but for a read-only search this is sufficient.

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?

The description is slightly verbose but every sentence carries unique information about matching, ordering, or usage guidance. There is no fluff or redundancy, and it is well-organized.

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?

The description fully equips the agent with the necessary context: when to call, how to phrase the query, what to expect in the results (candidates, ordering), and how to handle ambiguous results (show the user). No other tool description in the sibling set provides this level of contextual hand-holding.

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?

The 'query' parameter is thoroughly explained in the description, including quoting and matching behavior. The 'limit' parameter is only implicitly described via 'Returns at most 50 candidates' and the default of 10, but its purpose is intuitive and no further explanation is critical.

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 clearly states the verb 'resolve' and the resource 'company name or CUI', and explicitly distinguishes it from other tools by saying 'Use this first whenever the user names a company rather than giving its CUI.' This makes it obvious when to select this tool over siblings like get_company or list_companies.

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

Usage Guidelines5/5

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

It provides explicit guidance on when to use the tool ('first whenever the user names a company'), how to format queries (quoting for exact names), and what the matching behavior is (case/diacritics/folding). It also implies when not to use it by contrast with CUI-based lookups.

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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