datarust MCP Server (Model Context Protocol)

datarust.dev provides an official datarust Model Context Protocol (MCP) server enabling AI agents, LLMs, and coding assistants (Cursor, Claude Desktop, Antigravity, Cline) to directly invoke datarust machine learning, preprocessing, and dataset profiling tools.

MCP Server Endpoint & Manifest

• MCP Server Endpoint: https://datarust.dev/mcp
• In-Repository Implementation: Built in Rust in crates/datarust-mcp
• MCP Server Card (SEP-1649): https://datarust.dev/.well-known/mcp/server-card.json
• Agentic Resource Discovery (ARD): Indexed in https://datarust.dev/.well-known/ard.json under urn:air:datarust.dev:mcp:datarust
• Link Header: Every response advertises rel="ard" and rel="api-catalog" discovery headers.

Available Capabilities & Tools

The datarust MCP server exposes tools across classical machine learning and data profiling:

1. profile_dataset

Generates a comprehensive statistical profile of a dataset from CSV or raw tabular data.

• Inputs: CSV text, table rows, or file reference.
• Outputs: Column statistics (mean, std, median, min, max, missingness, cardinality), distribution shape, quality issues, target-leakage hints, and pairwise relationships (Pearson, Cramér’s V, point-biserial).

2. preprocess

Applies tabular preprocessing transformations with zero external dependencies.

• Encoders: OneHotEncoder, OrdinalEncoder, TargetEncoder, FrequencyEncoder.
• Scalers: StandardScaler, MinMaxScaler, RobustScaler, Normalizer.
• Imputers: SimpleImputer (mean, median, most_frequent, constant).

3. train_model

Trains classical estimators on feature matrix X and target vector y with exact scikit-learn parity.

• Models: LinearRegression, Ridge, Lasso, LogisticRegression, DecisionTreeClassifier, DecisionTreeRegressor, RandomForestClassifier, RandomForestRegressor, KMeans.
• Outputs: Model coefficients, tree structure, training metrics, and serialization payloads.

4. evaluate_metrics

Computes regression metrics (MSE, RMSE, MAE, R², max error) or classification metrics (accuracy, F1, precision, recall, confusion matrix, ROC-AUC, log loss).

Client Configuration

Registry Listings & Direct Installation

The datarust MCP server is listed in major MCP directories:

• Smithery Registry: https://smithery.ai/server/@genc-murat/datarust

  npx -y @smithery/cli install @genc-murat/datarust --client claude
  

• Agentic Resource Discovery (ARD): https://datarust.dev/.well-known/ard.json

Claude Desktop / Cursor

Add the datarust MCP server to your configuration (e.g. claude_desktop_config.json or Cursor settings):

{
  "mcpServers": {
    "datarust": {
      "url": "https://datarust.dev/mcp"
    }
  }
}

In-Browser WebMCP

On the datarust.dev website, client-side WebMCP is registered via navigator.modelContext so browser agents can invoke datarust estimators directly in-page.