Legal representatives and administrators of one company. This is personal data: it requires an API key, every call is recorded against that key, and it is never available anonymously. sources_present says which registers contributed, so an empty list means 'none on record from these sources', not 'this company has no officers'.
| Name | Required | Description | Default |
|---|---|---|---|
| cui | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by stating that the data is personal, requires an API key, is recorded per call, is never available anonymously, and that an empty sources_present result has a specific interpretation. It does not describe every possible caveat, but it covers the most important behaviors.
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 compact and front-loaded with the core purpose, followed by essential privacy and interpretation notes. It is slightly dense with multiple clauses, but every sentence contributes useful information rather than repeating the tool name or schema.
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?
The description is fairly complete for a one-parameter tool: it explains the resource, authentication prerequisites, and an important result-interpretation caveat. However, because there is no output schema, the description should clarify more about what the response contains besides sources_present, and it should define cui. These gaps prevent it from being fully 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 0%, and the description does not explain the only parameter 'cui'. The phrase 'of one company' loosely suggests a company identifier, but the description never states that cui is the company's unique identifier or what format it should take. This is a meaningful gap for a parameter that is required.
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 clearly identifies the resource: legal representatives and administrators of one company. It is not a tautology and gives useful domain meaning. However, it lacks an explicit verb such as 'retrieves' or 'returns', and it does not explicitly distinguish itself from siblings like get_legal or get_company.
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?
The description implies usage: it is for officer data of one company and requires an API key, so an agent can infer it is not for anonymous or multi-company lookups. But it provides no explicit when-to-use versus alternatives, and it does not name any sibling tool as a better choice in other cases.
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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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.
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.
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.
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.