Linear Regression MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
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.
Capabilities
Server capabilities have not been inspected yet.
Tools
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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Most tools have distinct purposes: checking categorical columns, getting column info, label encoding, training a model, and uploading a file. However, 'check_category_columns' and 'get_columns_info' could be slightly confused as both provide column-related information, but their specific focuses (categorical vs. general) help differentiate them.
The tools use a consistent snake_case format, which is good. However, the naming pattern is mixed: some are verb_noun (e.g., 'upload_file', 'train_linear_regression_model'), while others are noun_verb (e.g., 'check_category_columns', 'get_columns_info', 'label_encode_categorical_columns'). This inconsistency in verb placement reduces predictability.
With 5 tools, the count is reasonable for a linear regression server, covering data upload, inspection, preprocessing, and model training. It's slightly thin as it lacks tools for prediction, model evaluation beyond RMSE, or data splitting, but it's well-scoped for basic workflows.
The toolset covers core steps: data upload, column analysis, encoding, and model training. However, there are notable gaps: no tools for making predictions with the trained model, evaluating model performance beyond RMSE (e.g., R-squared), splitting data into train/test sets, or handling missing values. This limits end-to-end workflow coverage.