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yamariki-hub/japan-corporate-mcp

by Various

MCP Server for Japanese corporate data - gBizINFO, EDINET, e-Stat

Y

MCP

yamariki-hub/japan-corporate-mcp

Added 1 June 2026

Overview

An MCP (Model Context Protocol) server that provides access to Japanese corporate data from the official government sources gBizINFO, EDINET, and e-Stat. It exposes these datasets through a standardized interface for use in AI assistants and developer tools.

Best for

Best for
Developers building AI tools that need programmatic access to official Japanese corporate and financial data

Use cases

  • Retrieve Japanese company registration data via gBizINFO in an AI chatbot
  • Query EDINET financial documents for analysis within a development workflow
  • Integrate e-Stat statistical data into a Python-based agent or application

Notes

An MCP (Model Context Protocol) server that provides access to Japanese corporate data from the official government sources gBizINFO, EDINET, and e-Stat. It exposes these datasets through a standardized interface for use in AI assistants and developer tools.

0 stars on GitHub. Last updated 2026-02-22. Licensed MIT.

Use cases

  • Retrieve Japanese company registration data via gBizINFO in an AI chatbot
  • Query EDINET financial documents for analysis within a development workflow
  • Integrate e-Stat statistical data into a Python-based agent or application

Pros

  • Centralizes access to multiple authoritative Japanese government data sources
  • Uses the MCP standard, making it easy to plug into compliant AI assistants
  • Written in Python, straightforward to extend or modify

Cons

  • Zero stars on GitHub indicates minimal community adoption or testing
  • Limited to Japanese corporate and statistical data only
  • Requires understanding of MCP and the specific data source APIs

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Centralizes access to multiple authoritative Japanese government data sources
  • Uses the MCP standard, making it easy to plug into compliant AI assistants
  • Written in Python, straightforward to extend or modify

Cons

  • Zero stars on GitHub indicates minimal community adoption or testing
  • Limited to Japanese corporate and statistical data only
  • Requires understanding of MCP and the specific data source APIs