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kabuken

by io.github.Intellics-ai
OverviewDiscussionsAdmin

Get financial facts

get_financial_facts
Read-only

Return the XBRL financial facts reported in one EDINET filing of a Japanese listed company. Returns structured financial data with bilingual labels. When two facts in the result share the same standard label, those facts also carry label_en_verbose and label_ja_verbose with the full taxonomy label. Source: EDINET (https://disclosure2.edinet-fsa.go.jp/), PDL 1.0 (https://www.digital.go.jp/resources/open_data/public_data_license_v1.0). Data is processed by Kabuken, not raw unprocessed FSA data. Not investment advice.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of facts to return (1-500, default 100)
cursorNoOpaque next_cursor from the previous response; send it with the same doc_id, filters and limit to get the next page
doc_idYesEDINET document ID, e.g. S100ABCD
conceptsNoOptional list of XBRL concept names (fact_name) to filter to (e.g. 'jppfs_cor:NetSales')
statement_typeNoOptional filter by financial statement type (e.g. 'bs', 'pl', 'cf', 'ss')

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "description": "Opaque next_cursor from the previous response; send it with the same doc_id, filters and limit to get the next page",
+  "maxLength": 512,
+  "type": "string"
+}
  2. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already cover the read-only, non-destructive, closed-world profile, so the description only needs to add context – and it does: source, license, the caveat that data is Kabuken-processed rather than raw FSA data, and the bilingual label behavior. It omits pagination behavior, but cursor/limit are documented in the schema.

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

Conciseness4/5

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

The core purpose and scope are front-loaded in the first sentence, followed by the label-behavior note. The licensing and disclaimer sentences are longer than needed for tool selection but are legitimate provenance/disclaimer content, so the definition stays reasonably efficient.

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?

For a read-only retrieval tool with a fully documented schema and no output schema, the description covers provenance, label structure, and a partial picture of the return shape. It could say more about pagination and what a 'fact' record contains, but an agent has enough to call it correctly.

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 every parameter including the opaque cursor and concept filter is already explained. The description adds no syntax or semantic detail beyond the schema, which is acceptable at this coverage level but not value-adding.

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 ('Return the XBRL financial facts') plus the scope ('in one EDINET filing of a Japanese listed company'), which tells an agent exactly what data it yields. It does not explicitly distinguish itself from siblings like get_company_financials or get_calculation_tree, so the differentiation is implicit rather than stated.

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

Usage Guidelines2/5

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

The description never says when to choose this tool over get_company_financials, get_calculation_tree, or analyze_document, nor does it state any prerequisites or exclusions beyond the implied single-filing scope. Usage must be inferred from the scope phrase alone.

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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