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.
genpark-data-matrix-svd-pca-dimensionality-reducer-skill delivers zero-dependency, mathematically rigorous statistical modeling, time-series forecasting, and anomaly detection primitives engineered strictly using Python 3.9+ standard library.
Zero External Dependencies: Operates exclusively via pure Python (math, random, statistics, json). Zero pip install overhead, zero numpy/scipy compilation failures.
Enterprise Statistical Invariants: Implements formal Holt linear smoothing, modified Z-score & Tukey IQR anomaly bounds, Welch's t-test p-value estimations, multivariate linear regression via gradient descent, and Power Iteration PCA dimensionality reduction.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-statistical-zscore-iqr-anomaly-detector-skill
flowchart TD
DataStream["Time-Series & Multidimensional Numerical Stream"] --> AnomalyDetector["Statistical Z-Score & IQR Anomaly Detector"]
AnomalyDetector -->|Outlier Detected| FlagAnomaly["Flag Outlier & Alert Telemetry"]
AnomalyDetector -->|Clean Stream| ModelingBranch{"Analytical Objective"}
ModelingBranch -->|Trend Forecasting| HoltForecaster["Holt Linear Exponential Smoothing Forecaster"]
ModelingBranch -->|Supervised Fitting| LinearRegression["Multivariate Gradient Descent Regression Engine"]
ModelingBranch -->|Hypothesis Testing| WelchTest["Welch's Two-Sample T-Test Evaluator"]
ModelingBranch -->|Dimensionality Reduction| PCAReducer["Power Iteration SVD/PCA Dimension Reducer"]
HoltForecaster --> Synthesis["Agent Synthesis & Statistical Report"]
LinearRegression --> Synthesis
WelchTest --> Synthesis
PCAReducer --> Synthesisfrom client import DataMatrixSvdPcaDimensionalityReducer
# Initialize engine
engine = DataMatrixSvdPcaDimensionalityReducer()
# Execute self-testing benchmark suite
result = engine.run_benchmark_pca_reducer()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-data-matrix-svd-pca-dimensionality-reducer-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-data-matrix-svd-pca-dimensionality-reducer-skill/mcp_server.py"]
}
}
}This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-data-matrix-svd-pca-dimensionality-reducer-skill.gitStructured analysis API and remote MCP tool for text, JSON records and numeric series.
Analytics your AI agent can actually use. Track, experiment, and optimize via MCP.
Independent statistical feature evaluation for machine learning and forecasting. ML Referee tests up to 1,000 candidate features in one run against a declared baseline, helping researchers and AI agents decide which inputs to pursue, which bounded feature repairs merit further testing, and where more evidence is needed—without a separate ablation run for every feature. Use the MCP tools to discover input schemas, check access and credits, preflight a dataset, obtain a quote, submit an authorized evaluation, and retrieve structured results and reports. Results are scoped to the submitted evidence and evaluation design; they do not guarantee future model performance. Invite-only beta with manual approval. Public tool discovery requires no authentication; account and evaluation tools require OAuth through approved client connections and available evaluation credits. Connect directly to https://api.mlreferee.com/mcp. Website: https://mlreferee.com Request beta access: https://mlreferee.com/beta Agent setup and documentation: https://mlreferee.com/agents
Educational MCP server with 17 math/stats tools, visualizations, and persistent workspace
