Loading ToolBench
MCP server for Transformerlab providing an interface for AI assistants to interact with Transformerlab functionality, including model management, training, dataset operations, and evaluations.
TransformerLabMCP has 16 tools with consistent naming (verb_noun pattern) and basic descriptions. However, critical gaps undermine quality: (1) Most parameter descriptions are minimal (10-20 chars), violating the 50-200 char LLM-optimized baseline. (3) Output schemas are completely undocumented, LLMs cannot plan downstream calls. (4) Error handling returns generic error dicts without recovery guidance. (5) No enum constraints on multi-value params (eval_type, split, collection_name). (6) No pagination support despite list_* tools that could return large result sets. Example: start_finetuning has 6 params but only 3 have descriptions; learning_rate, num_epochs, batch_size lack context on valid ranges or units.
Add a document to the RAG system in Transformerlab.
Download a model from Hugging Face Hub to Transformerlab.
Get detailed information about a specific dataset.
Get the results of a specific evaluation.
Get detailed information about a specific model.
Get the status of a specific training job.
Import a dataset from Hugging Face Hub to Transformerlab.
No input schemas visible in source code. All tools show empty {} or minimal type info. LLMs cannot validate inputs or plan parameter passing.
Output schemas completely undocumented. No tool describes what fields it returns (e.g., does list_models return [name, id, size, type]?). LLMs cannot extract chaining IDs or plan downstream calls.
Parameter descriptions are minimal (10-20 chars) vs. LLM-optimized baseline of 50-200 chars. Example: 'The ID of the model to retrieve information for' (48 chars) lacks context on format, valid range, or when to use.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 51 | <=2025-11-25 | v2 |
List all available datasets in Transformerlab.
List all model evaluations in Transformerlab.
List all available models in Transformerlab.
List all RAG collections in Transformerlab.
List all training jobs in Transformerlab.
Perform a RAG query using a specific model and collection.
Start a model evaluation job in Transformerlab.
Start a fine-tuning job in Transformerlab.
Stop a running training job.
No enum constraints on multi-value parameters. eval_type accepts 'basic|comprehensive|custom' but is free-form string; split accepts 'train|test|validation' but is free-form. LLMs hallucinate invalid values.
Error handling returns generic dicts without recovery guidance. Example: {'error': 'Failed to list models: ...'} tells LLM nothing about retry eligibility, user-fixable issues, or next steps. Violates recovery-guide pattern.