datarust-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
datarust workspace
This repository is a Cargo workspace hosting the datarust ecosystem: a collection of independent, separately-versioned crates for classical data-science and machine-learning workloads in Rust.
Crates
Crate | Path | Description |
[datarust] | Scikit-learn-style preprocessing and classical ML in Rust. Standard/minmax/robust scalers, encoders, imputers, PCA, linear models, decision trees & random forests (bit-exact sklearn parity), clustering, pipelines — zero external dependencies by default. | |
[datarust-profile] | One-call data profiling and data-quality reports. Column statistics, distribution shape, pairwise relationships (Pearson, Cramér's V, point-biserial), target-leakage hints, quality findings, CSV ingestion, single-pass streaming, a CLI — HTML/JSON/terminal output. | |
[datarust-mcp] | Model Context Protocol (MCP) server implementation for Claude Desktop, Cursor, and AI agents. Exposes data profiling, training, and preprocessing over stdio JSON-RPC. |
Each crate has its own README.md, CHANGELOG.md, version, and release track,
and is published independently to crates.io.
Related MCP server: Kaggle Dataset Analyst
Model Context Protocol (MCP) Server
This repository contains the official MCP server implementation in crates/datarust-mcp.
Running Locally (Stdio)
cargo run --release -p datarust-mcpClaude Desktop / Cursor Setup
Add to your claude_desktop_config.json:
{
"mcpServers": {
"datarust": {
"command": "cargo",
"args": ["run", "--release", "--manifest-path", "/path/to/datarust/Cargo.toml", "-p", "datarust-mcp"]
}
}
}Available Tools
profile_dataset: Profiles CSV data, computing descriptive stats, distribution shapes, and quality checks.train_linear_regression: Fits OLS linear regression with exact scikit-learn parity.scale_features: Feature scaling via StandardScaler, MinMaxScaler, or RobustScaler.evaluate_metrics: Computes R², MSE, RMSE, MAE, and accuracy metrics.
Agent Platform Configs & Official Skills
This repository provides pre-configured agent instructions and official Agent Skills for autonomous coding agents:
Cursor Rules:
.cursorrules&.cursor/rules/datarust.mdcClaude Instructions:
CLAUDE.md&.claude/CLAUDE.mdWindsurf Rules:
.windsurf/rules/rules.mdCopilot Instructions:
.github/copilot-instructions.mdAgent Plugins Manifest:
plugin.jsonOfficial Skills (skills.sh):
skills.json— self-published skills inskills/:
Documentation
Website: https://datarust.dev
Developer Portal: https://datarust.dev/developers/
Docs (book):
book/— rendered at https://datarust.dev/docs/MCP Server: https://datarust.dev/docs/mcp/ — verified listing on Smithery
OpenAPI 3.1 Spec: https://datarust.dev/openapi.json
Blog:
blog/— release stories and field notesArchitecture:
crates/datarust/ARCHITECTURE.mdRoadmap:
crates/datarust/ROADMAP.md
Working in this workspace
# Build / test every crate
cargo build --workspace
cargo test --workspace
# Work on a single crate
cargo test -p datarust --all-features
cargo test -p datarust-profile --features serde
# Build the static docs/blog site (Node 22+)
npm install
npm run build && npm run checkThe workspace root Cargo.toml carries a [patch.crates-io] entry that points
datarust-profile at the local source tree of datarust during development.
This patch is ignored by cargo publish, so published crates depend on the
crates.io release as normal.
License
MIT — see each crate's LICENSE.
This server cannot be deployed
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