e-Stat Dashboard MCP Server

An MCP (Model Context Protocol) server to interact with the e-Stat Dashboard API, providing Japanese statistical data to LLMs.

Installation & Usage

Using uv (Recommended)

You can run the server directly without installing it using uvx:

uvx e-stat-dashboard-mcp

via Claude Desktop config

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "e-stat-dashboard": {
      "command": "uvx",
      "args": ["e-stat-dashboard-mcp"]
    }
  }
}

Manual Installation (pip)

pip install e-stat-dashboard-mcp
python -m e_stat_dashboard_mcp.server

Local Development

Clone the repository and install dependencies:

git clone https://github.com/takurot/e-stat-dashboard-mcp.git
cd e-stat-dashboard-mcp
uv sync

Running the Server (Local)

uv run python -m e_stat_dashboard_mcp.server

Or via MCP Inspector:

npx @modelcontextprotocol/inspector uv run python -m e_stat_dashboard_mcp.server

Tools

search_indicators(keyword: str) -> str

Search for statistical indicators.

• Example: search_indicators(keyword="失業率")
• Returns a Markdown list of matching indicators with codes.

get_stats_data(indicator_code: str, from_date: str = None, to_date: str = None, cycle: str = None) -> str

Get statistical data for a specific indicator.

• Example: get_stats_data(indicator_code="0301010000020020010", from_date="20230100")
• Returns a JSON string of flattened data points.

analyze_stats(data_json: str, analysis_type: str, ...) -> str

Analyze the retrieved data.

• Types:
  • summary: Basic stats (mean, median, variance, quartiles, CV)
  • cagr (or growth): Compound Annual Growth Rate
  • yoy: Year-Over-Year growth
  • trend: Linear trend (slope, R², direction)
  • anomaly: Latest-point anomaly detection (Z-score)
  • correlation: Correlation between two series
• Examples:
  • analyze_stats(data_json=..., analysis_type="cagr")
  • analyze_stats(analysis_type="correlation", data_json=[{...}], data_json_b=[{...}])
    • Or server-side fetch: analyze_stats(analysis_type="correlation", indicator_code="CODE_A", indicator_code_b="CODE_B")

generate_graph_code(data_payload, graph_type: str, title: str, x_label: str = "Date", y_label: str = "Value") -> str

Generate Python code to visualize data using Matplotlib (with Japanese font support).

• Types: line, bar, scatter
• data_payload: JSON string or already-parsed list of records (e.g., [{"date": "2020-01", "value": 10}]). If omitted, use data_uri.
• Examples:
  • generate_graph_code(data_payload=[{"date": "2020-01", "value": 10}], graph_type="line", title="Unemployment Rate")
  • generate_graph_code(data_uri=..., graph_type="line", title="Unemployment Rate")

list_regions(parent_region_code: str = None) -> str

List regions (Prefectures, etc).

• Example: list_regions(parent_region_code="00000") (Lists prefectures)

Configuration

Environment variables can be set in .env or passed directly. Prefix is ESTAT_.

Development

Run tests:

uv run pytest

Run linter:

uv run ruff check .