How old each dataset is, how many companies filed in each year, what fraction of the register carries an activity code, and the mapping from every financial field name to its Romanian label. Call this when the user asks how current or how complete the data is.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 transparently enumerates what the tool reports and implicitly signals this is a read-only metadata query. It does not describe exact formatting, but for a zero-parameter tool the listed outputs are enough to set expectations.
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. The output contents are listed first, followed by a crisp usage trigger, making it easy for an agent to scan and immediately understand when to invoke the tool.
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 parameterless metadata tool with no output schema, the description gives enough context to make the correct call: it identifies the specific meta-questions it answers. It does not specify response formatting or exact schema, but that is not critical for tool selection or 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?
The tool has zero parameters, so there is nothing for the description to clarify beyond what the schema shows. The baseline is 4 for a parameterless tool, and the description does not introduce any contradictory or confusing parameter concepts.
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 specifies what the tool returns: dataset age, filing counts per year, activity-code coverage, and financial field-name mappings. It is distinct from the sibling data-retrieval tools by presenting aggregate metadata about the register rather than a specific company or filing. The use case is also explicit: measure data currency and completeness.
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 explicitly states when to call this tool: when the user asks how current or how complete the data is. It does not name alternatives or state when not to use it, but the trigger context is clear and sufficient for a zero-parameter metadata tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.