Statsource MCP Server
by jamie7893
The Statsource MCP Server is a Model Context Protocol server that provides statistical analysis capabilities. It enables LLMs to analyze data from various sources, calculate statistics, and generate predictions.
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What is Statsource MCP Server?
The Statsource MCP Server is a tool that allows AI models to perform statistical analysis and generate ML predictions based on user data from various sources like PostgreSQL databases, CSV files, or APIs. It provides a standardized interface for LLMs to access and utilize statistical functions.
How to use Statsource MCP Server?
The server can be installed using uv, Docker, or pip. After installation, it can be configured for use with applications like Claude.app by specifying the command and arguments in the application's settings. Environment variables like API_KEY, DB_CONNECTION_STRING, and DB_SOURCE_TYPE can be used for configuration.
Key features of Statsource MCP Server
Statistical analysis via the
get_statisticstoolML prediction capabilities
Support for various data sources (CSV, database, API)
Filtering and grouping options for data analysis
Feature suggestion tool (
suggest_feature)
Use cases of Statsource MCP Server
Analyzing user data to identify trends and patterns
Generating ML predictions based on historical data
Providing statistical insights to LLMs for decision-making
Automating data analysis tasks
Integrating statistical analysis into AI-powered applications
FAQ from Statsource MCP Server
What data sources are supported?
What data sources are supported?
The server supports CSV files (uploaded to statsource.me), databases (PostgreSQL), and APIs.
How do I specify the data source?
How do I specify the data source?
You can specify the data source using the data_source and source_type arguments in the get_statistics tool. Alternatively, you can configure the DB_CONNECTION_STRING and DB_SOURCE_TYPE environment variables.
What statistics can I calculate?
What statistics can I calculate?
Valid statistics options include: 'mean', 'median', 'std', 'sum', 'count', 'min', 'max', 'describe', 'correlation', 'missing', 'unique', 'boxplot'.
How can I filter the data?
How can I filter the data?
You can use the filters, date_column, start_date, and end_date arguments to filter the data based on specific criteria.
How do I suggest a new feature?
How do I suggest a new feature?
Use the suggest_feature tool, providing a clear description, use case, and suggested priority for the feature.