Loading ToolBench
A Discord chatbot for debugging Kubernetes logs and cluster diagnostics using MCP-based Kubernetes tools with supervisor-worker agent system
This MCP server has only ONE tool: 'human_assistance'. The tool definition is visible in src/agent.py and is registered with a basic input schema. However, the definition quality is severely limited: the tool name lacks a clear action verb (should be 'request_human_assistance'), the description is minimal (12 chars, below the 20-char hard threshold), parameter descriptions are absent, and there is no output schema documented. The tool is designed to interrupt the LLM and request human input via Discord, which is a valid pattern but is implemented with minimal metadata that would help an LLM understand when and how to invoke it. The server's primary purpose appears to be a Discord chatbot for Kubernetes debugging with LangGraph state management, but the MCP interface itself (the tool) is undersupecified.
Request human assistance when needed
Tool description is 12 characters ('Request human assistance when needed'), below the 20-character hard threshold for minimum viability.
Tool name 'human_assistance' does not start with an action verb. Should be 'request_human_assistance' or 'ask_for_human_help' to clearly signal intent to the LLM. 90% of A+ production tools start with action verbs (get, list, create, search, update, delete, send).
Input parameter 'request' has a description but lacks context for when the LLM should use this tool vs others. The description does not explain: What does the tool do? When should it be called? What happens after the human responds? What will the response be?
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 27 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 11 | - | v1 |
No output schema documented. LLMs need to know what fields to expect in the response so they can plan downstream logic (e.g., does human_assistance return a structured response with a 'response' field, or does it trigger a GraphInterrupt and continue via Command.resume()?). Current code shows GraphInterrupt handling in main.py but this is not reflected in tool metadata.
Tool is not idempotent. Calling human_assistance twice with the same input will prompt for human input twice, which could lead to duplicate interrupts and confusion. Tool description should state this risk and the LLM should understand not to retry on ambiguous failures.
No error handling or recovery guidance. What happens if human_assistance is called but no human responds within a timeout? What should the LLM do next? Currently the code will hang or raise a GraphInterrupt with no guidance on recovery.