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

export EDINET_API_KEY=your_key_here

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}

        # 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 asyncio
from 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 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())

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())

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.run(main())

# Search companies
edinet-mcp search トヨタ

# Fetch income statement
edinet-mcp statements -c E02144 -p 2024

# Screen multiple companies
edinet-mcp screen E02144 E01777 E02529 --sort-by ROE

# Compare across periods (xbrl-diff)
edinet-mcp diff -c E02144 -p1 2023 -p2 2024

# Start MCP server
edinet-mcp serve

import asyncio
from 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 label
stmt.income_statement["売上高"]       # → {"当期": 45095325, "前期": 37154298}
stmt.income_statement.get("営業利益") # → {"当期": 5352934} or None
stmt.income_statement.labels          # → ["売上高", "営業利益", ...]

# DataFrame export
stmt.balance_sheet.to_polars()    # → polars.DataFrame
stmt.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 preserved
stmt.income_statement.raw_items   # original pre-normalization data

from edinet_mcp import get_taxonomy_labels

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

EDINET API → Parser (XBRL/TSV) → Normalizer (taxonomy.yaml) → MCP Server
                                        ↓
                              StatementData["売上高"]
                              calculate_metrics(stmt)
                              compare_periods(stmt)

git clone https://github.com/ajtgjmdjp/edinet-mcp
cd edinet-mcp
uv sync --extra dev
uv run pytest -v           # 336 tests
uv run ruff check src/