Loading ToolBench
R statistical computing repository for sparse penalized confidence-based classification with support for L1 (Lasso), SCAD, and MCP penalties
This R package exposes 15 statistical/ML functions as tools but lacks MCP-compliant tool registration, descriptions, and schemas. Tools are inferred from R function signatures in funtion_ncv.R; no explicit MCP tool definitions found. Descriptions exist in comments but are technical/academic rather than LLM-optimized. Parameter types are documented in comments (e.g. 'numeric vector', 'matrix') but not in formal JSON Schema. No output schemas documented. No error handling guidance. This is a research codebase, not a production MCP server.
Simple linear neural network module with single linear layer (no bias) for classification
Computes classification accuracy on test data using a trained model
Computes stable logistic loss function ℓ(z) = log(1 + exp(-z)) using torch operations
Numerically stable implementation of logistic loss function with separate handling for positive and negative values
Trains a linear neural network classifier using penalized confidence-based logistic loss with SGD optimization
Performs K-fold cross-validation to select optimal regularization parameter lambda for penalized confidence-based classifier
Fits penalized confidence-based linear classifier with L1, SCAD, or MCP penalties using proximal gradient descent
No MCP tool registration found. Tools are R functions in funtion_ncv.R with no explicit MCP server wrapper, tool definitions, or JSON Schema. Cannot be invoked as MCP tools.
No input schemas in JSON Schema format. Parameter types documented in comments ('numeric vector', 'matrix', 'torch tensor') but not in formal schema. LLMs cannot validate or constrain inputs.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 29 | 2026-07-28+ | v2 |
Fits penalized confidence-based linear classifier with early stopping based on weight and loss convergence criteria
Proximal operator for L1 penalty (soft-thresholding)
Proximal operator for MCP (Minimax Concave Penalty) with validation that learning rate is less than shape parameter
Proximal operator for SCAD (Smoothly Clipped Absolute Deviation) penalty with three regions
Unified interface for proximal operators supporting L1, SCAD, and MCP penalties
Numerically stable sigmoid function with separate computation for positive and negative values
Applies soft-thresholding (proximal operator for L1 penalty) to coefficients
Evaluates variable selection performance by computing True Positive Rate (TPR) and False Discovery Rate (FDR) comparing estimated coefficients against true coefficients
No output schemas documented. Functions return lists or numeric vectors with no specification of field names, types, or structure. Agents cannot plan downstream calls.
Descriptions are academic/technical (e.g. 'Proximal operator for SCAD penalty with three regions') rather than LLM-optimized. Missing WHEN to use, prerequisites, and actionable guidance for agent selection.
No error handling or recovery guidance. Functions may fail (e.g. 'lr >= a' check in prox_MCP) but no tool-level error messages guide LLM recovery.