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kabuken

by io.github.Intellics-ai
OverviewDiscussionsAdmin

Search companies

search_companies
Read-only

Find Japanese listed companies by name and get their identifiers (entity code, ticker). Send query for one name, or queries (up to 20 names) to resolve many names in one call; each name returns its own list of matches. Returns matching companies with bilingual names and identifiers.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of results to return per name (1-20, default 10)
queryNoCompany name text to search, English or Japanese
cursorNoOpaque next_cursor from the previous response; send it with the same query and limit to get the next page
queriesNoCompany name texts to search in one call, English or Japanese. Use instead of query. Not combined with cursor

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum number of results to return (1-20, default 10)"New value: +"Maximum number of results to return per name (1-20, default 10)"
    • addedInput schema / properties / queries
      Added value: +{
+  "description": "Company name texts to search in one call, English or Japanese. Use instead of query. Not combined with cursor",
+  "items": {
+    "maxLength": 100,
+    "type": "string"
+  },
+  "maxItems": 20,
+  "minItems": 1,
+  "type": "array"
+}
    • removedInput schema / required
      Removed value: -[
-  "query"
-]
  2. Changed1 schema field changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "description": "Opaque next_cursor from the previous response; send it with the same query and limit to get the next page",
+  "maxLength": 512,
+  "type": "string"
+}
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish readOnly/openWorld/destructive profile, and the description adds useful behavioral context: bilingual company names, per-name result lists, and identifier returns. It does not discuss matching behavior (exact vs partial) or pagination in prose, but the added return-shape information is substantive.

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?

Two dense sentences, front-loaded with purpose and followed by the key usage distinction. No wasted text, and every clause carries relevant information.

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

Completeness4/5

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

With no output schema, the description does a reasonable job of conveying return content (matching companies with bilingual names and identifiers, per-name lists). Pagination via cursor is only covered in the schema, so minor incompleteness remains.

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

Parameters3/5

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

Schema description coverage is 100%, so parameter meanings are already fully documented. The description reinforces the query/queries distinction and the 20-name batch limit, but mostly restates what the schema already provides.

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?

States a specific verb and resource ('Find Japanese listed companies by name') plus the returned identifiers (entity code, ticker). It is clear, but it does not explicitly differentiate itself from siblings such as get_company or screen_companies.

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

Usage Guidelines4/5

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

Explains the two usage modes clearly: 'query' for one name and 'queries' (up to 20) for batch resolution, noting each name returns its own match list. It lacks explicit when-to-use guidance versus related siblings, but the single-vs-batch routing is well covered.

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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