Enter a KVK number to get a complete company group tree, listing parents, subsidiaries, and ultimate beneficial owners, even when entities lack an LEI.
Group structure for a KVK number: parents, subsidiaries and UBO as a tree. Where GLEIF stops because an entity holds no LEI, this keeps going. NOTE: contains personal data. PAID.
| Name | Required | Description | Default |
|---|---|---|---|
| kvk_number | Yes |
Changes observed during successful MCP inspections.
v1.6.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does a decent job: it discloses that the output is a tree, that it extends beyond GLEIF/LEI coverage, that it contains personal data, and that it is a paid tool. It could add more detail about data sensitivity handling or response characteristics, but the key behavioral caveats are present.
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?
Two sentences with no filler. The main purpose is front-loaded, followed by the coverage boundary and critical caveats about personal data and cost. Every clause contributes meaningful guidance for an agent.
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 one-parameter read-style tool with no output schema, the description explains the return shape (tree of parents, subsidiaries, UBO) and critical constraints (personal data, paid, LEI gap-filling). It is slightly incomplete around input format details and explicit sibling routing, but it is largely sufficient 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 should compensate, but it only restates that the input is a KVK number without specifying format, validation, or accepted variations. The single parameter name already communicates 'kvk_number', so the description adds little beyond what the schema shows.
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 tool's output: a group structure tree containing parents, subsidiaries, and UBOs for a KVK number. It is specific about the resource and content, but it does not explicitly differentiate from sibling tools like nl_company_profile or nl_company_search, though the tree/group-structure focus makes it fairly distinguishable.
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?
It provides a clear usage context: use this when GLEIF would stop due to missing LEI, since this tool continues the hierarchy. It names GLEIF as an alternative and gives an implied condition for selection, but it does not explicitly mention when-not-to-use relative to sibling tools or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.