This server has significant quality gaps across naming, descriptions, schemas, and error handling. While tool definitions are explicitly visible in main.py, they lack the rigor expected of production-grade agent tooling. Tool names are reasonably clear (upload_dataset, train_model, clean_dataset) but descriptions are minimal (10-70 chars, well below the 194-char baseline for A+ tools). Input schemas are present but lack proper typing metadata (no 'type' fields visible in JSON Schema format, only parameter names and descriptions in docstrings). Parameter descriptions are present but generic, they state WHAT a parameter is named, not WHY an agent should choose a particular value or what constraints apply. Output schemas are completely undocumented: agents have no way to know what structure train_model or clean_dataset return. Error handling is weak, tools return free-text error strings rather than structured error objects with recovery guidance. The server lacks critical security patterns (no credential isolation, no permission gates, no audit trails). No tool demonstrates confirmation-request or dry-run patterns despite several tools performing irreversible operations (train_model modifies model_cache, clean_dataset modifies the dataset in-place). Parameter interdependencies (e.g., model_type=classification determines valid model_name values) are documented in code but not exposed to the agent. This is a D-grade server, better than completely broken, but not production-ready.

• Add explicit JSON Schema definitions with 'type', 'required', 'enum', and 'minimum'/'maximum' for all parameters. Move from docstring annotations to proper schema declarations.
• Document output schemas for every tool. Use the JSON Schema format or describe the return structure explicitly: 'Returns {success: bool, rows_uploaded: int, shape: [rows, cols]}'.
• Expand tool descriptions to 150 - 250 chars. Include WHEN to call the tool, prerequisites, and any side effects. Example: 'Train a machine learning model. Call this after clean_dataset. model_type must match the data: classification for discrete targets, regression for continuous. This operation is irreversible and overwrites any previous model for the same dataset.'
• Add enum constraints for model_type (classification|regression) and model_name, with the valid choices visible to the agent. Document the dependency: 'If model_type=classification, model_name may be: logistic_regression, random_forest, svm, knn, decision_tree. If model_type=regression, model_name may be: linear_regression, random_forest, svm, decision_tree.'
• Implement structured error responses. Instead of 'Dataset not found', return {status: 'error', code: 'DATASET_NOT_FOUND', message: 'Dataset "xyz" not found.', recovery: 'Call upload_dataset() with the CSV content, or call download_kaggle_dataset() to import a Kaggle dataset.'}.
• Add confirmation-request or dry-run support for destructive tools. Implement a 'confirm_clean_dataset' tool that returns statistics (rows to remove, missing value counts, columns to encode) before actually modifying the cache. Pattern: 'Call confirm_clean_dataset first, review the impact, then call clean_dataset with confirmed=true.'