Linear Regression MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| upload_file | 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_info | This function gives information about columns. Returns: String which contains column names. |
| check_category_columns | This function check if data has categorical columns. Returns: String which contains list of categorical columns. |
| label_encode_categorical_columns | This function label encodes all the categorical columns. Returns: String which confirms success of encoding process. |
| train_linear_regression_model | This function trains linear regression model. Args: Takes input for output column name. Returns: String which contains the RMSE value. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Latest Blog Posts
MCP directory API
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'
If you have feedback or need assistance with the MCP directory API, please join our Discord server