Edinet MCP

edinet-mcp

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

日本語版READMEはこちら

📝 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) | stockprice-mcp (stock prices & FX)

Building a high-throughput pipeline, batch-parsing thousands of filings, or

need SEC EDGAR coverage too? See xbrl-facts

— a Rust iXBRL engine for SEC + EDINET with byte-range provenance.

What it looks like

Ask your AI assistant (with edinet-mcp connected):

You: トヨタの最新の決算と、会社が挙げている主要なリスクを教えて

Claude: トヨタ自動車 (E02144) の有価証券報告書（2026年6月10日提出）によると——

- 売上高: 50.7兆円（前期 48.0兆円、約 +5.5%）

- 事業等のリスク（有報本文より）: 自動車市場の競争激化 — CASE などの技術革新が進むことで競争は一層激化し、業界再編につながる可能性も指摘 …

Every number and passage above is fetched from the actual EDINET filing via get_financial_statements / get_narrative — nothing comes from the model's memory.

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/semiannual reports (有価証券報告書, 四半期報告書, 半期報告書) plus extraordinary (臨時報告書) and large shareholding (大量保有報告書) filings
• Automatic normalization: stmt["売上高"] works regardless of accounting standard
• Bilingual labels: stmt["Revenue"] works too (case-insensitive), and the MCP tool supports language='en' for fully English output
• 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)
• Narrative sections: extract 事業等のリスク, MD&A, 経営方針 and more as plain text (get_narrative)
• Evidence receipts (optional): machine-verifiable claim-to-source records for key figures, backed by the xbrl-facts Rust engine (get_receipts, pip install edinet-mcp[receipts])
• MCP server with 11 tools for Claude Desktop and other AI tools

Why edinet-mcp?

Unlike commercial EDINET data APIs, edinet-mcp is fully free and local: the XBRL parser runs on your machine, the only credential you need is a free EDINET API key from the FSA, and every number is traceable to the original filing. No paid tiers, no usage caps beyond EDINET's own rate limits, Apache-2.0 licensed.

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}

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())

Narrative Sections (定性情報)

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())

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'}")

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": 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())

MCP Server

Add to your AI tool's MCP config:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json)

{

"mcpServers": { "edinet": { "command": "uvx", "args": ["edinet-mcp", "serve"], "env": { "EDINET_API_KEY": "your_key_here" } } } }

</details>

Cursor (~/.cursor/mcp.json)

{

"mcpServers": { "edinet": { "command": "uvx", "args": ["edinet-mcp", "serve"], "env": { "EDINET_API_KEY": "your_key_here" } } } }

</details>

Claude Code

claude mcp add edinet -- uvx edinet-mcp serve

Then set EDINET_API_KEY in your environment

</details>

Then ask your AI: "トヨタの最新の営業利益を教えて"

Available MCP Tools

Note: The period parameter is the filing year, not the fiscal year. Japanese companies with a March fiscal year-end file annual reports in June of the following year (e.g., FY2024 → filed 2025 → period="2025").

CLI

# 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

API Reference

EdinetClient

All client methods are async. Use async with for proper resource cleanup:

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())

Filing

Filing objects returned by get_filings() have the following attributes:

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)

StatementData

Each financial statement (BS, PL, CF) is a StatementData object with dict-like access:

# 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

Normalization

edinet-mcp automatically normalizes XBRL element names across accounting standards:

| IFRS | `Re