Loading ToolBench
Adversarial multi-agent eval harness for Claude API/OpenAI or any LLM agent pipelines, exposed as MCP tools for use in Cursor and Antigravity
Three tools with complete input schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. Tool names follow verb_noun pattern (gauntlet_find_agents, gauntlet_eval_prompt, gauntlet_eval_file) which is good. However, parameter descriptions are sparse or missing entirely. gauntlet_find_agents has only one parameter with a description; gauntlet_eval_prompt and gauntlet_eval_file lack descriptions for most parameters (goal, agent_description, agent_system_prompt, agent_code, agent_api_key, agent_model, agent_provider, mode, runs, success_criteria). No output schemas documented anywhere. Tool descriptions are adequate (100-150 chars) but lack guidance on when to use each tool vs. alternatives. No error handling patterns visible in code sample.
Paste Python agent code and Gauntlet will extract the system prompt then run an adversarial eval automatically.
Run an adversarial eval on any LLM agent defined by a model name and system prompt. Returns pass rate, per-scenario verdicts, and recommendations.
Scan the current workspace recursively for Python agent files. Detects system prompts, goals, model names, and providers automatically. Returns a numbered list — user just replies with the number to run eval. Trigger this when the user says 'find', 'find agents', 'find gauntlet', 'scan', or 'what agents do I have'.
Missing parameter descriptions for 9 of 11 parameters across gauntlet_eval_prompt and gauntlet_eval_file. Parameters like 'goal', 'agent_description', 'agent_system_prompt', 'agent_code', 'agent_model', 'agent_provider', 'mode', 'runs', 'success_criteria' have no descriptions. LLMs cannot infer parameter meaning from names alone.
No output schemas documented for any tool. LLMs need to know what fields to expect in responses to plan downstream calls and extract data correctly. Rubric baseline: 100% of A+ tools have documented return types.
Parameter 'agent_api_key' exposed as a tool parameter. Credentials should never appear in tool parameters, use server-side secret injection via environment variables. Agent traces log every parameter, leaking secrets into logs and prompt history.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 57 | 2026-07-28+ | v2 |
No error handling patterns visible. Tool descriptions do not explain what to do if evaluation fails, if workspace scan finds no agents, or if API key is invalid. Rubric requires: 'Error responses must tell the LLM what to do next.'
Parameter 'runs' accepts 1-20 but no description explains what 'runs' means (number of evaluation iterations?). Parameter 'mode' has enum values [standard, adversarial, full] but no description of what each mode does or when to use each.