Every filed financial year for one company: turnover, profit (signed — negative is a loss), employees, and the full balance sheet, all in lei. latest_filed_year says how recent the filings go, so a year with no row reads as 'not filed yet' rather than 'no data'.
| 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 behavioral burden. It clearly discloses signed profit semantics (negative is a loss), the unit of currency (lei), and the important interpretation of missing rows as 'not filed yet' rather than 'no data'. This goes well beyond a simple 'get financials' statement.
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 two sentences with no wasted words. It front-loads the core purpose and field list in the first sentence and adds a crucial interpretation caveat in the second. Every clause earns its place.
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?
For a single-parameter tool with no output schema and no annotations, the description is notably complete. It names the main data fields, explains profit sign, specifies currency, and clarifies missing-year semantics. It could mention the response shape or exact field names, but nothing critical is missing for correct invocation.
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 description coverage is 0%, so the description must compensate for explaining 'cui'. The description only references 'for one company', which hints that cui identifies a company, but it never explicitly states that cui is the company identifier or what format/type is expected. This is a meaningful gap for an agent.
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 states a specific action and resource: retrieving every filed financial year for one company, including turnover, signed profit, employees, and balance sheet. The scope ('for one company') and the detailed field list make it easy to distinguish from broader sibling tools like company_stats 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: call this when you need a company's financial history and balance sheet data. However, it does not explicitly discuss when to avoid it or name any alternative tools, leaving the selection decision somewhat implied rather than guided.
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.