Loading ToolBench
An MCP server for RL training: watch, diagnose and steer a running job from an agent or a shell
rl-mcp provides 19 tools with consistent naming (verb_noun pattern) and reasonable descriptions (avg ~80 chars). All tools have input schemas with typed parameters and descriptions. However, output schemas are not documented, LLMs cannot predict return structure. Error handling is minimal; no recovery guidance or actionable error messages. Parameter descriptions lack constraints (enums, ranges, formats). No tool annotations (readOnlyHint/destructiveHint) despite clear risk levels. Composition is sound (single responsibility), but descriptions could be more LLM-optimized per pattern:tool-description baseline (target 50-200 chars; many here are 40-60 chars).
Save a checkpoint of the training run
Get an artifact from the training run
Get events from the training run
Get metrics from the training run
Get current parameter values
Get the current status of the training run
List artifacts produced by the training run
List available commands for the training run
Output schemas not documented. LLMs cannot predict return structure (fields, types, pagination). Baseline: 100% of A+ tools document return types.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Tools marked READ_ONLY, WRITE, DESTRUCTIVE in metadata but not exposed to LLM via schema. Agents cannot reason about side effects.
Parameter descriptions lack constraints. 'env_id' (shot, video) has no range or validation hint. 'last_n' (get_metrics, get_events) has no min/max. 'smooth' (plot_metrics) has no range. Baseline: descriptions should state format, range, allowed values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 58 | 2026-07-28+ | v2 |
List available metrics from the training run
List available parameters for the training run
Pause the training run
Generate a plot of training metrics
Resume the training run
Run a command on the training run
Set parameter values in the training run
Take a screenshot of the training environment
Stop the training run
Switch to a different training session
Generate a video of the training environment
Minimal error handling. No recovery guidance, no actionable error messages, no error classification (retryable vs fatal). Baseline: errors must tell LLM what to do next.
Descriptions too brief for LLM optimization. 'Pause the training run' (pause, resume, stop) is 27 chars; baseline 50-200 chars. Missing WHEN to use, prerequisites, side effects.