mcp-server-stats 0.2.0

A Model Context Protocol server for statistical analysis

Statsource MCP Server

A Model Context Protocol server that provides statistical analysis capabilities. This server enables LLMs to analyze data from various sources, calculate statistics, and generate predictions.

The statistics tool connects to our analytics API and allows AI models to perform statistical analysis and generate ML predictions based on user data, whether it's in a PostgreSQL database or a CSV file.

Available Tools

get_statistics

Analyze data and calculate statistics or generate ML predictions based on provided parameters.

Arguments:

• columns (list of strings, required): List of column names to analyze or predict (Ask user for exact column names).
• data_source (string, optional): Path to data file (uploaded to statsource.me), database connection string (ask user for exact string), or API endpoint. If not provided, uses DB_CONNECTION_STRING from env config if set.
• source_type (string, optional): Type of data source ("csv", "database", or "api"). If not provided, uses DB_SOURCE_TYPE from env config if set.
• table_name (string, optional but required if source_type is "database"): Name of the database table to use (Ask user for exact table name).
• statistics (list of strings, optional): List of statistics to calculate (required for query_type="statistics"). Valid options include: 'mean', 'median', 'std', 'sum', 'count', 'min', 'max', 'describe', 'correlation', 'missing', 'unique', 'boxplot'.
• query_type (string, optional, default="statistics"): Type of query ("statistics" or "ml_prediction").
• periods (integer, optional): Number of future periods to predict (required for query_type="ml_prediction").
• filters (dict, optional): Dictionary of column-value pairs to filter data (e.g., {"status": "completed", "region": ["North", "East"]}).
• groupby (list of strings, optional): List of column names to group data by before calculating statistics (e.g., ["region", "product_category"]).
• options (dict, optional): Dictionary of additional options for specific operations.
• date_column (string, optional): Column name containing date/timestamp information for filtering and time-series analysis.
• start_date (string or datetime, optional): Inclusive start date for filtering (ISO 8601 format, e.g., "2023-01-01").
• end_date (string or datetime, optional): Inclusive end date for filtering (ISO 8601 format, e.g., "2023-12-31").

Key Usage Notes:

• Data Sources: For CSV, the user must upload the file to statsource.me first and provide the filename. For databases, ask the user for the exact connection string and table name. Never guess or invent connection details.
• Configuration: If data_source and source_type are not provided, the tool will attempt to use DB_CONNECTION_STRING and DB_SOURCE_TYPE from the environment configuration (see below).
• Filtering/Grouping: Use filters, groupby, date_column, start_date, and end_date to analyze specific subsets of data.

suggest_feature

Suggest a new feature or improvement for the StatSource analytics platform.

Arguments:

• description (string, required): A clear, detailed description of the suggested feature
• use_case (string, required): Explanation of how and why users would use this feature
• priority (string, optional): Suggested priority level ("low", "medium", "high")

Installation

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run mcp-server-stats.

Using PIP

Alternatively you can install mcp-server-stats via pip:

pip install mcp-server-stats

After installation, you can run it as a script using:

python -m mcp_server_stats

Or use the console script:

mcp-server-stats

Docker Support

To use the Docker option mentioned in the configuration section, you'll need to build and publish the Docker image first:

Building the Docker image locally

docker build -t statsource/mcp .

Running the Docker image locally

docker run -i --rm statsource/mcp

Publishing to Docker Hub

If you want to make the image available for everyone:

docker login
docker push statsource/mcp

Configuration

Configure for Claude.app

Add to your Claude settings:

Using uvx

"mcpServers": {
  "statsource": {
    "command": "uvx",
    "args": ["mcp-server-stats"]
  }
}

Using docker

"mcpServers": {
  "statsource": {
    "command": "docker",
    "args": ["run", "-i", "--rm", "jamie78933/statsource-mcp"]
  }
}

Using pip installation

"mcpServers": {
  "statsource": {
    "command": "python",
    "args": ["-m", "mcp_server_stats"]
  }
}

Environment Variables

You can configure the server using environment variables in your Claude.app configuration:

"mcpServers": {
  "statsource": {
    "command": "python",
    "args": ["-m", "mcp_server_stats"],
    "env": {
      "API_KEY": "your_api_key",
      "DB_CONNECTION_STRING": "postgresql://username:password@localhost:5432/your_db",
      "DB_SOURCE_TYPE": "database"
    }
  }
}

Available environment variables:

• API_KEY: Your API key for authentication with statsource.me
• DB_CONNECTION_STRING: Default database connection string
• DB_SOURCE_TYPE: Default data source type (usually "database")

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx mcp-server-stats

Or if you've installed the package in a specific directory or are developing on it:

cd path/to/servers/
npx @modelcontextprotocol/inspector python -m mcp_server_stats

Contributing

We encourage contributions to help expand and improve mcp-server-stats. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.

Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements to make mcp-server-stats even more powerful and useful.

License

mcp-server-stats is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.