Edinet Mcp

import asynciofrom 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}
        # English labels work too (case-insensitive)        assert stmt.income_statement["Revenue"] == revenue        print(stmt.income_statement.labels_en)        # ["Revenue", "Cost of Sales", "Gross Profit", ...]
        # 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())

import asynciofrom edinet_mcp import EdinetClient
async def main():    async with EdinetClient() as client:        # 事業等のリスク as plain text        risks = await client.get_narrative("E02144", "business_risks")        print(risks.text[:200])
        # Other sections: mdna, business_policy, description_of_business,        # corporate_governance, research_and_development
asyncio.run(main())

import asynciofrom 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())

import asynciofrom 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())

import asynciofrom 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.run(main())

import asynciofrom edinet_mcp import EdinetClient
async def main():    async with EdinetClient(        api_key="...",        # or EDINET_API_KEY env var        cache_dir="~/.cache/edinet-mcp",        rate_limit=0.5,       # requests per second    ) as client:        # Search        companies: list[Company] = await client.search_companies("query")        company: Company = await client.get_company("E02144")
        # Filings        filings: list[Filing] = await client.get_filings(            start_date="2024-01-01",            edinet_code="E02144",            doc_type="annual_report",        )
        # Financial statements (by edinet_code + period)        stmt: FinancialStatement = await client.get_financial_statements(            edinet_code="E02144",            period="2024",  # Filing year (not fiscal year)        )
        # Or get the most recent filing (within past 365 days)        stmt = await client.get_financial_statements(edinet_code="E02144")
        df = stmt.income_statement.to_polars()  # Polars DataFrame        df = stmt.income_statement.to_pandas()  # pandas DataFrame (optional dep)
asyncio.run(main())

for filing in filings:    print(filing.description)    # "有価証券報告書－第121期(...)"    print(filing.filing_date)    # datetime.date(2025, 6, 18)    print(filing.doc_id)         # "S100VWVY"    print(filing.company_name)   # "トヨタ自動車株式会社"    print(filing.period_start)   # datetime.date(2024, 4, 1)    print(filing.period_end)     # datetime.date(2025, 3, 31)

# Dict-like access by Japanese labelstmt.income_statement["売上高"]       # → {"当期": 45095325, "前期": 37154298}stmt.income_statement.get("営業利益") # → {"当期": 5352934} or Nonestmt.income_statement.labels          # → ["売上高", "営業利益", ...]
# DataFrame exportstmt.balance_sheet.to_polars()    # → polars.DataFramestmt.balance_sheet.to_pandas()    # → pandas.DataFrame (requires pandas)stmt.balance_sheet.to_dicts()     # → list[dict]len(stmt.balance_sheet)           # number of line items
# Raw XBRL data preservedstmt.income_statement.raw_items   # original pre-normalization data

from edinet_mcp import get_taxonomy_labels
# Discover available labelslabels = get_taxonomy_labels("income_statement")# [{"id": "revenue", "label": "売上高", "label_en": "Revenue"}, ...]