EDINET MCP Server

edinet-mcp

EDINET XBRL parsing library and MCP server for Japanese financial data.

📝 日本語チュートリアル: Claude に聞くだけで上場企業の決算がわかる (Zenn)

Part of the Japan Finance Data Stack: edinet-mcp (securities filings) | tdnet-disclosure-mcp (timely disclosures) | estat-mcp (government statistics) | boj-mcp (Bank of Japan) | stockprice-mcp (stock prices & FX)

What is this?

edinet-mcp provides programmatic access to Japan's EDINET financial disclosure system. It normalizes XBRL filings across accounting standards (J-GAAP / IFRS / US-GAAP) into canonical Japanese labels and exposes them as an MCP server for AI assistants.

• Search 5,000+ listed Japanese companies
• Retrieve annual/quarterly financial reports (有価証券報告書, 四半期報告書)
• Automatic normalization: stmt["売上高"] works regardless of accounting standard
• Financial metrics (ROE, ROA, profit margins) and year-over-year comparisons
• Parse XBRL into Polars/pandas DataFrames (BS, PL, CF)
• Multi-company screening: Compare financial metrics across up to 20 companies
• Cross-period diff (xbrl-diff): Compare financial statements across periods with change amounts (増減額) and growth rates (増減率)
• MCP server with 9 tools for Claude Desktop and other AI tools

Quick Start

Installation

pip install edinet-mcp
# or
uv add edinet-mcp
# or with Docker
docker run -e EDINET_API_KEY=your_key ghcr.io/ajtgjmdjp/edinet-mcp serve

Get an API Key

Register (free) at EDINET and set:

export EDINET_API_KEY=your_key_here

30-Second Example

import asyncio
from edinet_mcp import EdinetClient

async def main():
    async with EdinetClient() as client:
        # Search for Toyota
        companies = await client.search_companies("トヨタ")
        print(companies[0].name, companies[0].edinet_code)
        # トヨタ自動車株式会社 E02144

        # Get normalized financial statements
        stmt = await client.get_financial_statements("E02144", period="2025")

        # Dict-like access — works for J-GAAP, IFRS, and US-GAAP
        revenue = stmt.income_statement["売上高"]
        print(revenue)  # {"当期": 45095325000000, "前期": 37154298000000}

        # See all available line items
        print(stmt.income_statement.labels)
        # ["売上高", "売上原価", "売上総利益", "営業利益", ...]

        # Export as DataFrame
        print(stmt.income_statement.to_polars())

asyncio.run(main())

Financial Metrics

import asyncio
from edinet_mcp import EdinetClient, calculate_metrics

async def main():
    async with EdinetClient() as client:
        stmt = await client.get_financial_statements("E02144", period="2025")
        metrics = calculate_metrics(stmt)
        print(metrics["profitability"])
        # {"売上総利益率": "25.30%", "営業利益率": "11.87%", "ROE": "12.50%", ...}

asyncio.run(main())

Multi-Company Screening

import asyncio
from edinet_mcp import EdinetClient, screen_companies

async def main():
    async with EdinetClient() as client:
        result = await screen_companies(
            client,
            ["E02144", "E01777", "E01967"],  # Toyota, Sony, Keyence
            period="2025",
            sort_by="営業利益率",  # Sort by operating margin
        )
        for r in result["results"]:
            print(f"{r['company_name']}: {r['profitability']['営業利益率']}")
        # 株式会社キーエンス: 51.91%
        # ソニーグループ株式会社: 11.69%
        # トヨタ自動車株式会社: 9.98%

asyncio.run(main())

Cross-Period Diff

import asyncio
from edinet_mcp import EdinetClient, diff_statements

async def main():
    async with EdinetClient() as client:
        result = await diff_statements(
            client, "E02144",
            period1="2024", period2="2025",
        )
        for d in result["diffs"][:5]:
            print(f"{d['科目']}: {d['増減額']:+,.0f} ({d['増減率']})")
        # 売上高: +7,941,027,000,000 (+21.38%)
        # 営業利益: +1,204,832,000,000 (+28.44%)
        # ...

asyncio