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find-missing

by io.github.NKO-STUDIO

find_missing

Query whether an entity with a real LEI has a high-confidence match in the national commercial registry its declared jurisdiction would expect.

Args:
    identifier_type: one of "lei", "company_number", "name".
    identifier: the LEI, registration number, or legal name to look up.
    api_key: a free key obtained from POST https://api.nkodatalabs.com/v0/keys.
    jurisdiction: optional ISO-3166 alpha-2 country code (e.g. "GB", "ES", "FR").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYes
identifierYes
jurisdictionNo
identifier_typeYes

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys that this is a read-style query and mentions that an api_key can be obtained from a free POST endpoint, but it does not disclose return values, error behavior, side effects, or what happens when jurisdiction is omitted. The meaning of 'real LEI' is also left unexplained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact: one clear purpose sentence followed by a scannable Args list. Every line earns its place, and the most important behavioral statement is front-loaded. No unnecessary fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Invocation details are essentially complete: required arguments, valid values, optional jurisdiction, and API key acquisition are all covered. However, the tool has no output schema, and the description does not explain what the tool returns (e.g., a boolean, a match record, or a confidence score) or how errors or missing matches are signaled. This is a meaningful gap for an agent deciding whether the result is usable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema itself provides no parameter descriptions, but the description fully compensates by documenting every parameter: allowed values for identifier_type, the semantics of identifier, how to obtain api_key, and the format and optionality of jurisdiction. This is exactly the kind of parameter-level detail an agent needs beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific action—'Query whether'—and a well-defined resource: whether an entity with a real LEI has a high-confidence match in a national commercial registry. It also lists the accepted identifier types, which clarifies the tool's scope. The phrase 'real LEI' and 'declared jurisdiction' is slightly ambiguous, but the core purpose is clear. There are no sibling tools to differentiate from.

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

Usage Guidelines3/5

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

The description implies the usage scenario—checking registry match existence for a given identifier—but does not explicitly state when to use this tool versus alternatives or when not to use it. Since no sibling tools are provided, the lack of explicit alternatives is less damaging, but clear usage conditions are still absent.

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

A4/5.0
Disambiguation5/5

With only one tool, there is no possibility for an agent to confuse it with another tool. The single tool's purpose is clearly defined, and its arguments are explicit.

Naming Consistency5/5

The tool name follows a clear verb_noun pattern, which is predictable and descriptive. With a single tool, consistency is not a practical concern, but the naming is appropriately concise.

Tool Count4/5

The server has only one tool, which is below the typical 3-15 range, but it is appropriate for the server's extremely focused purpose of checking registry matches. It feels slightly thin for broader applications but is not unreasonable.

Completeness5/5

The server's entire domain is to query whether an entity has a high-confidence registry match, and this single tool fully addresses that need. There are no obvious missing operations within this narrow scope.

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