Schema | Linear Regression MCP

Linear Regression MCP

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Server capabilities have not been inspected yet.

Functions exposed to the LLM to take actions

Name	Description
upload_fileC	This function read the csv data and stores it in the class variable. Args: Absolute path to the .csv file. Returns: String which shows the shape of the data.
get_columns_infoC	This function gives information about columns. Returns: String which contains column names.
check_category_columnsC	This function check if data has categorical columns. Returns: String which contains list of categorical columns.
label_encode_categorical_columnsC	This function label encodes all the categorical columns. Returns: String which confirms success of encoding process.
train_linear_regression_modelC	This function trains linear regression model. Args: Takes input for output column name. Returns: String which contains the RMSE value.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/HeetVekariya/Linear-Regression-MCP'

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