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Report a data issue

report_data_issue

Records a report that a figure or field for a listed company looks wrong or missing, with the filing, concept and context it concerns. Returns a report_id and status. The report stores only the text written in it and the account that sent it, and is reviewed by the Kabuken team.

Input Schema

NameRequiredDescriptionDefault
doc_idNoEDINET document id of the filing the figure comes from.
concept_idNoXBRL concept of the figure, such as jppfs_cor:NetSales.
context_refNoXBRL context reference of the figure, such as CurrentYearDuration.
descriptionYesWhat is wrong or missing, with the value shown and the value expected. Do not include personal data.
entity_codeYesEDINET code of the company concerned, such as E02144.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false and idempotentHint=false. The description adds real context beyond them: what gets stored (only the entered text and the sending account), that a report_id and status are returned, and that the Kabuken team reviews the report. It omits any mention of quota/auth constraints, which matters given the get_account_quota sibling.

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?

Three sentences, front-loaded with the purpose, then the return value, then the storage/review caveat. No redundancy with the name or title, and every sentence carries new 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?

Return values are described inline in place of an output schema, and the storage/review behavior answers the main questions an agent would have about a write tool. Rate limits or duplicate-submission behavior are the only notable omissions.

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 all five parameters are already documented with formats and examples. The description only frames them collectively ('the filing, concept and context it concerns') without adding format, priority, or optionality guidance, so the baseline 3 applies.

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: records a report that a company figure/field is wrong or missing, naming the filing, concept and context involved. That is concrete enough to distinguish it from every sibling tool, all of which are read/analysis operations. It stops short of explicitly contrasting with a sibling, so a 4 rather than 5.

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?

Usage is only implied by the phrase 'looks wrong or missing' – the agent can infer this is for suspected data errors, but there is no explicit when-to-use, when-not-to-use, or named alternative. Given that no sibling tool overlaps, the gap is small but real.

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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