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
data_source (string, optional): Path to data file, database connection string, or API endpoint
source_type (string, optional): Type of data source ("csv", "database", or "api")
statistics (list of strings, optional): List of statistics to calculate (for statistical analysis)
query_type (string, optional): Type of query ("statistics" or "ml_prediction")
periods (integer, optional): Number of future periods to predict (for ML predictions)

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

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", "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.

This server cannot be installed

security - not tested

license - not found

quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables LLMs to perform statistical analysis and generate ML predictions on user data from databases or CSV files through a Model Context Protocol server.

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