Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-linear-regression-gradient-descent-engine-skill Run a linear regression on x=[1,2,3], y=[2,4,6]"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-linear-regression-gradient-descent-engine-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-time-series-exponential-smoothing-forecaster-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 LinearRegressionGradientDescentEngine
# Initialize engine
engine = LinearRegressionGradientDescentEngine()
# Execute self-testing benchmark suite
result = engine.run_benchmark_linear_regression()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-linear-regression-gradient-descent-engine-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-linear-regression-gradient-descent-engine-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-linear-regression-gradient-descent-engine-skill.gitThis server cannot be deployed
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