Loading ToolBench
Endgame ML toolkit — build ML pipelines through natural language. Use discovery resources for browsing models, presets, metrics, and visualizers. Use tools to load data, train models, evaluate, visualize, and export pipelines.
This server exhibits severe gaps in definition quality across nearly all dimensions. While it claims 13 tools, the source code provided contains only configuration files, build scripts, and partial server stub (endgame/mcp/server.py is truncated mid-docstring). No actual tool implementations are visible. Tool definitions appear to be inferred from file paths and the tools list provided, not from verifiable source code. Descriptions exist but are generic and lack actionable detail. No input schemas, parameter descriptions, or output schema documentation are visible in the provided code. The server is built on fastmcp (a Python framework), but the critical tool registration code is not shown. Based on available evidence: all tools lack verifiable schemas (0 points), descriptions are present but minimal (20-40 chars, scoring 30-40), and naming follows a reasonable verb_noun pattern (60-70). This is a case where I cannot verify what I cannot see, per the hard rules, tools without visible definitions are capped at 50, and inferred tools score lower.
Advanced training features including ensemble methods and AutoML
Discover available evaluation metrics
Discover available machine learning models in the Endgame catalog
Discover available hyperparameter presets for models
Discover available visualization tools
Evaluate a trained model on test data using various metrics
Export trained models to various formats (ONNX, pickle, etc.)
Apply guardrails and safety checks to models and data
NO INPUT SCHEMAS VISIBLE, All 13 tools lack verifiable parameter schemas, types, and constraints. Cannot determine what inputs each tool accepts, their types, or validation rules. This is a critical blocker for LLM-driven invocation.
MINIMAL DESCRIPTIONS, All tool descriptions are under 50 characters and lack detail on WHEN to use each tool, WHAT it does precisely, and any prerequisites. Examples: 'Discover available machine learning models in the Endgame catalog' (60 chars) is present but generic; 'Generate predictions from a trained model' (42 chars) is vague. No explanation of dependencies (e.g., predict must follow train_model) or outputs.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 34 | 2026-07-28+ | v2 |
Load data from various sources (CSV, Parquet, ARFF, OpenML)
Generate predictions from a trained model
Apply preprocessing transformations to data
Train a machine learning model on loaded data
Create visualizations of data, models, and results
NO PARAMETER DESCRIPTIONS VISIBLE, The provided source does not show any parameter definitions. Without visible parameter schemas or descriptions, cannot verify that LLMs understand what inputs each tool accepts, their formats, constraints, or allowed values.
NO OUTPUT SCHEMA DOCUMENTATION, Cannot determine what structure or fields each tool returns. LLMs need documented return types to plan follow-up calls. For example, does train_model return a model_id that predict can use? Unknown.
TOOL DEFINITIONS NOT VISIBLE IN SOURCE, The source code provided is truncated and incomplete. Only config files, build scripts, and a partial server stub are shown. Actual tool implementations in endgame/mcp/tools/*.py are referenced but not included.
GENERIC TOOL NAMES, 'predict' is vague (predict what? from where?). 'visualize' is ambiguous (visualize what type of data?). 'guardrails' is a noun, not a verb_noun pattern. Names like predict_with_model, visualize_results, and apply_safety_checks would be clearer.
POTENTIAL MULTI-RESPONSIBILITY TOOL, 'advanced_training' likely combines ensemble methods, AutoML, and hyperparameter tuning into one tool. Should be split into separate concerns so agents can compose them granularly.
MISSING DEPENDENCY HINTS, No documentation of prerequisites. For example, train_model and evaluate_model likely require load_data to run first, but this is not stated in descriptions. Similarly, predict needs a trained model, but that dependency is not called out.
NO ERROR HANDLING GUIDANCE VISIBLE, Cannot assess whether tools return structured error messages with recovery hints. If a tool fails, does it tell the LLM what to try next, or just a generic error?