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estat-mcp

e-Stat API client and MCP server for Japanese government statistics (政府統計の総合窓口)

Install / Use

claude mcp add ajtgjmdjp -- npx -y github:ajtgjmdjp/estat-mcp

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

80/100

Supported Platforms

Claude Code
Claude Desktop

Our assessment of estat-mcp

estat-mcp scores 80/100 on our quality scale, 3616th of 4,655 Development & Engineering skills we index.

Its MCP Server is 9.4 KB long, well organised into 39 sections with 15 code examples: a thorough specification that gives an agent plenty to work with.

It has 10 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
29/30
Structure
20/20
Description
12/15
Adoption
4/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so estat-mcp is actively maintained.
  • Our last check on 2026-09-29 found the source still online.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

estat-mcp compared with similar skills

All 4 of these similar skills score higher than estat-mcp; compare them before choosing.

SkillScoreStarsUpdatedFormat
estat-mcp (this skill)by ajtgjmdjp80102mo agoMCP Server
Agent-Reachby Panniantong100107.3k7d agoCLAUDE.md
taste-skillby Leonxlnx100107.1ktodayCLAUDE.md
headroomby headroomlabs-ai10078.5k1d agoCLAUDE.md
ai-job-searchby ai-job-search-dev10068.5ktodayCLAUDE.md

Frequently asked questions

How do I install estat-mcp?
Run claude mcp add ajtgjmdjp -- npx -y github:ajtgjmdjp/estat-mcp. The install tabs above show the steps for each supported agent.
Which AI agents does estat-mcp work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is estat-mcp safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is estat-mcp still maintained?
The repository was last updated about 2 months ago, so estat-mcp is actively maintained.

estat-mcp

PyPI Python 3.10+ License: Apache-2.0 ClawHub

e-Stat (政府統計の総合窓口) API client and MCP server for Japanese government statistics.

📝 日本語チュートリアル: Claude に聞くだけで GDP や CPI がわかる (Zenn)

What is this?

estat-mcp provides programmatic access to Japan's official statistics portal (e-Stat), which hosts 3,000+ statistical tables covering population, economy, prices, labor, agriculture, and regional data. It exposes these as both a Python async client and an MCP server for AI assistants.

  • Search statistics by keyword (人口, GDP, CPI, etc.)
  • Retrieve metadata to understand table structure (表章事項, 分類事項, 時間軸, 地域)
  • Fetch statistical data with automatic pagination for large datasets
  • Type-safe Pydantic models with Polars/pandas DataFrame export
  • Rate limiting built-in for API compliance
  • MCP server with 4 tools for Claude Desktop and other AI tools

Quick Start

Installation

pip install estat-mcp
# or
uv add estat-mcp

Get an API Key

Register (free) at e-Stat API and set:

export ESTAT_APP_ID=your_app_id_here

30-Second Example

import asyncio
from estat_mcp import EstatClient

async def main():
    async with EstatClient() as client:
        # Search for population statistics
        tables = await client.search_stats("人口")
        print(tables[0].name)  # 人口推計

        # Get metadata
        meta = await client.get_meta(tables[0].id)
        print(f"Time periods: {[t.name for t in meta.time_items]}")

        # Fetch data (Tokyo, 2024)
        data = await client.get_data(
            tables[0].id,
            cd_area="13000",   # Tokyo
            cd_time="2024000", # 2024
            limit=100
        )
        
        # Export as DataFrame
        df = data.to_polars()
        print(df)

asyncio.run(main())

CLI Quick Start

# Test API connectivity
estat-mcp test

# Search for CPI statistics
estat-mcp search "消費者物価指数" --limit 10

# Fetch data for a table
estat-mcp data 0003410379 --cd-area 13000 --format table

# Start MCP server
estat-mcp serve

MCP Server

Add to your AI tool's MCP config:

<details> <summary><b>Claude Desktop</b> (~⁠/Library/Application Support/Claude/claude_desktop_config.json)</summary>
{
  "mcpServers": {
    "estat": {
      "command": "uvx",
      "args": ["estat-mcp", "serve"],
      "env": {
        "ESTAT_APP_ID": "your_app_id_here"
      }
    }
  }
}
</details> <details> <summary><b>Cursor</b> (~⁠/.cursor/mcp.json)</summary>
{
  "mcpServers": {
    "estat": {
      "command": "uvx",
      "args": ["estat-mcp", "serve"],
      "env": {
        "ESTAT_APP_ID": "your_app_id_here"
      }
    }
  }
}
</details>

Then ask your AI: "日本の人口統計を教えて" or "東京の最新CPIは?"

Available MCP Tools

| Tool | Description | |------|-------------| | search_statistics | キーワードで統計テーブルを検索 | | get_statistic_meta | テーブル構造(表章事項・分類事項・時間軸・地域)を取得 | | get_statistic_data | 統計データを取得(フィルタ・ページネーション対応) | | get_all_statistic_data | 全ページ自動取得(max_pages制限付き) |

Python API

Search → Metadata → Data Flow

import asyncio
from estat_mcp import EstatClient

async def main():
    async with EstatClient() as client:
        # 1. Search for statistics tables
        tables = await client.search_stats("消費者物価指数", limit=5)
        # → [StatsTable(id="0003410379", name="消費者物価指数", ...), ...]

        stats_id = tables[0].id

        # 2. Get metadata to understand structure
        meta = await client.get_meta(stats_id)
        print(f"Table items: {[i.name for i in meta.table_items]}")
        print(f"Time periods: {[t.name for t in meta.time_items]}")
        print(f"Areas: {[a.name for a in meta.area_items]}")

        # 3. Fetch data with filters
        data = await client.get_data(
            stats_id,
            cd_area="13000",      # Tokyo only
            cd_time="2024000",    # 2024 only
            limit=1000
        )

        # 4. Export to DataFrame
        df = data.to_polars()
        print(df)

asyncio.run(main())

Automatic Pagination

For large datasets, use get_all_data() to automatically fetch all pages:

# Fetch up to 10 pages (safety limit)
all_data = await client.get_all_data(
    stats_id,
    max_pages=10,
    cd_cat01="100"  # Filter by classification
)
print(f"Fetched {len(all_data.values)} / {all_data.total_count} records")

Polars Export

# Requires: pip install estat-mcp[polars]
df = data.to_polars()

CLI Reference

# Search for statistics tables
estat-mcp search <keyword> [options]
  --limit, -n     Max results (default: 20)
  --format, -f    Output format: table|json (default: table)

# Fetch statistical data
estat-mcp data <stats_id> [options]
  --limit, -n       Max records (default: 100)
  --format, -f      Output format: table|json (default: table)
  --cd-tab          Filter by table item code
  --cd-time         Filter by time code
  --cd-area         Filter by area code
  --cd-cat01        Filter by classification code

# Test API connectivity
estat-mcp test

# Show version
estat-mcp version

# Start MCP server
estat-mcp serve [--transport stdio|sse]

Available Data

e-Stat provides access to 3,000+ statistical tables:

| Category | Examples | |----------|----------| | Population | 人口推計、国勢調査、将来人口推計 | | Economy | GDP、企業統計、商業統計、工業統計 | | Prices | 消費者物価指数(CPI)、企業物価指数 | | Labor | 労働力調査、賃金構造基本統計調査 | | Trade | 貿易統計、国際収支統計 | | Agriculture | 農業センサス、漁業センサス、林野統計 | | Regional | 小地域・地域メッシュ、都道府県・市区町村データ |

Integration with edinet-mcp

estat-mcp works alongside edinet-mcp for comprehensive Japanese financial data:

  • edinet-mcp: Company financial statements (有価証券報告書)
  • estat-mcp: Macroeconomic and demographic statistics

Example workflow:

  1. Use edinet-mcp to get Toyota's revenue from EDINET
  2. Use estat-mcp to get auto industry production statistics
  3. Compare company performance against industry trends

API Reference

EstatClient

from estat_mcp import EstatClient

async with EstatClient(
    app_id="...",           # or ESTAT_APP_ID env var
    timeout=60.0,           # request timeout
    rate_limit=0.5,         # requests per second
) as client:
    # Search
    tables: list[StatsTable] = await client.search_stats("人口", limit=20)
    
    # Metadata
    meta: StatsMeta = await client.get_meta("0003410379")
    
    # Data (single page)
    data: StatsData = await client.get_data(
        "0003410379",
        cd_area="13000",
        cd_time="2024000",
        limit=1000
    )
    
    # Data (all pages)
    all_data: StatsData = await client.get_all_data(
        "0003410379",
        max_pages=10
    )

StatsData

# Access values
for v in data.values:
    print(v.value)              # The numeric value
    print(v.time_code)          # Time dimension code
    print(v.area_code)          # Area dimension code
    print(v.classification_codes)  # Dict of classification codes

# Export
json_data = data.to_dicts()     # list[dict]
df = data.to_polars()           # polars.DataFrame (requires polars)

Response Format

e-Stat API uses XML-style conventions in JSON:

{
  "TABLE_INF": [
    {"@code": "110", "@name": "人口", "@unit": "人", "@level": "1"}
  ],
  "VALUE": [
    {"@tab": "110", "@time": "2024000", "@area": "13000", "$": "13960000"}
  ]
}
  • @-prefixed keys are XML attributes
  • $ contains the text content

estat-mcp handles this automatically — you get clean Python objects.

Development

git clone https://github.com/ajtgjmdjp/estat-mcp
cd estat-mcp
uv sync --extra dev
uv run pytest -v           # Run tests
uv run ruff check src/     # Lint
uv build                   # Build package

Project Structure

estat-mcp/
├── src/estat_mcp/
│   ├── __init__.py      # Public API exports
│   ├── client.py        # EstatClient (async HTTP)
│   ├── models.py        # Pydantic models (StatsTable, StatsData, etc.)
│   ├── server.py        # MCP server (FastMCP)
│   └── cli.py           # Command-line interface
├── tests/               # Test suite
├── examples/            # Example scripts
├── pyproject.toml       # Project configuration
└── README.md

Data Attribution

This project uses data from e-Stat (政府統計の総合窓口), operated by the Statistics Bureau of Japan (総務省統計局). e-Stat data is provided under terms compatible with CC BY 4.0.

出典: 政府統計の総合窓口(e-Stat)(https://www.e-stat.go.jp/)

このサービスは、政府統計総合窓口(e-Stat)のAPI機能を使用していますが、サービスの内容は国によって保証されたものではありません。

This service uses the API of the Portal Site of Official Statistics of Japan (e-Stat). The content of this service is not guaranteed by the Japanese government.

Related Projects

  • edinet-mcp — EDINET financial data MCP server
  • jfinqa — Japanese financial QA benchmark

License

Apache-2.0

<!-- mcp-name: io.github.ajtgjmdjp/estat-mcp -->

Related Skills

View on GitHub
GitHub Stars10
CategoryDevelopment
Updated2mo ago
Forks5

Languages

Python

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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