Loading ToolBench
MCP server for benchmarking local LLM fine-tuning hardware performance
This server has a single tool 'execute_skill_action' with virtually no usable definition. The tool description is generic (28 chars, below the 20-char hard floor for scoring). No input schema is visible in the source code, the mcp_server.py file contains only a JSON status message with no tool registration, parameter definitions, or schema. The client.py and example_usage.py files show a benchmark_fine_tuning() method with parameters (model_name, dataset_samples) and return fields (vram_usage_gb, tokens_per_sec, tuning_status), but these are NOT exposed as MCP tool schemas. Tool definitions are inferred from external code, not explicitly registered in the MCP server.
Execute a skill action for LLM fine-tuning hardware benchmarking
No input schema visible. Tool parameters (model_name, dataset_samples) are inferred from client.py, not declared in MCP server.
Tool description is 28 characters ('Execute a skill action for LLM fine-tuning hardware benchmarking'), generic and below baseline (194 chars avg). Does not explain WHAT the tool does, WHEN to use it, or what it returns.
No parameter descriptions. client.py shows model_name and dataset_samples parameters, but MCP server does not expose them with type, range, or constraint information. LLM cannot infer valid values.
No output schema documented. Return fields (vram_usage_gb, tokens_per_sec, tuning_status) are visible in client.py but not declared in MCP tool definition. LLM cannot plan downstream actions or extract results.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 22 | 2026-07-28+ | v2 |
Tool name 'execute_skill_action' is generic and vague. Does not start with a clear action verb (get_, create_, benchmark_, etc.). 'execute_skill_action' could mean anything, LLM cannot infer intent from name alone.