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ChenJellay

Data Analytics MCP Toolkit

by ChenJellay
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PYTHONPATHNoEnvironment variable to ensure the 'src' directory is in the Python search path for module resolution.

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
load_dataA
Ingest data from CSV or JSON (inline string or URL). Returns data_id and schema summary.
Use the returned data_id in subsequent tools (clean_data, plot_*, train_*, etc.).
clean_dataB
Clean dataset: optionally drop NA rows and z-score normalize numeric columns.
Updates the dataset in place; returns data_id and row count.
plot_barC

Bar chart: x_column as categories, y_column as values (or count of x if y_column omitted).

plot_lineC

Line chart: x_column on x-axis, one or more y_columns as lines.

plot_scatterC

Scatter plot of x_column vs y_column.

plot_histogramC

Histogram of a numeric column (distribution).

plot_boxC

Box plot: single numeric column, or all numeric columns if column is omitted.

plot_heatmapC

Heatmap of correlation matrix. If columns omitted, uses all numeric columns.

train_test_splitB
Split dataset into train and test. Returns train_data_id and test_data_id
for use in train_* and evaluate_* tools.
train_linear_regressionC

Fit a linear regression model. Returns model_id for evaluate_regression.

train_logistic_regressionB

Fit a logistic regression classifier. Returns model_id for evaluate_classification.

train_kmeansC

Fit K-means clustering. Returns model_id for evaluate_clustering.

evaluate_regressionC

Compute MSE and R² for a regression model on test data.

evaluate_classificationC

Compute accuracy for a classification model on test data.

evaluate_clusteringC

Compute silhouette score for a clustering model on test data.

run_analyticsB
High-level tool: describe what you want (e.g. "show distribution of sales",
"predict price from square_feet", "cluster into 4 groups") and provide the data
(CSV/JSON string or URL). The server picks the right pipeline and returns
either a chart (chart_base64, chart_type) or ML metrics and model summary.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
list_pipelinesList available analytics pipelines with short descriptions.
pipeline_visualizationSteps: 1) load_data(source, format) 2) clean_data(data_id) 3) plot_histogram/plot_bar/plot_line/plot_scatter/plot_box/plot_heatmap(data_id, column(s)). Or use run_analytics(intent, data_source).
pipeline_regressionSteps: 1) load_data 2) clean_data 3) train_test_split(data_id, target_column) 4) train_linear_regression(train_data_id, target_column) 5) evaluate_regression(model_id, test_data_id). Or use run_analytics(intent, data_source) with intent like 'predict Y from X'.
pipeline_classificationSteps: 1) load_data 2) clean_data 3) train_test_split 4) train_logistic_regression 5) evaluate_classification. Or use run_analytics with intent like 'classify' or 'predict category'.
pipeline_clusteringSteps: 1) load_data 2) clean_data 3) train_kmeans(data_id, n_clusters) 4) evaluate_clustering(model_id, data_id). Or use run_analytics with intent like 'cluster into k groups'.

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