Works with the AI tools you already use
Tool-Calling Failure Debugger
by heyhridyansh
Diagnose tool-calling failures, schema mismatches, and unsafe agentic workflows with a structured audit framework.
$14.99
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See it in action
You say
My agent is failing to update user records. It keeps trying to call the 'get_user' tool instead of 'update_user', and when it does pick the right one, the 'user_id' is a string instead of an int.
Your agent does
Root Cause: Description Ambiguity & Schema Mismatch. Verdict: Material tool-calling failures. Score: 62/100. Findings:
- High: 'update_user' description lacks 'when to use' constraints, causing 'get_user' to dominate selection.
- Medium: Schema expects integer ID; prompt lacks type-casting instruction.
What you get
About this skill
The problem
Tool-calling workflows fail in production due to subtle schema drifts, ambiguous descriptions, or unsafe retry logic. Developers struggle to trace why an LLM selected the wrong tool, hallucinated arguments, or ignored approval gates during complex multi-step actions.
What it does
- Analyzes tool-call traces to pinpoint where discovery, selection, or argument construction diverged from intent.
- Audits JSON schemas and tool descriptions for ambiguities that trigger routing errors or validation failures.
- Evaluates safety controls including permission scoping, idempotency keys, and manual approval gating for destructive actions.
- Detects architectural risks like tool-output injection, infinite loops, and state loss in multi-turn sequences.
- Generates a remediation plan with a 100-point safety score and specific test cases for failure recovery.
Why this beats prompting it yourself
Standard prompts often miss edge cases like partial failure recovery or tool-output injection risks. This skill applies a structured 25-step audit workflow and a specialized failure catalog to ensure no part of the tool-execution lifecycle is overlooked.
Use cases
- Debugging why an agent repeatedly calls the same tool with invalid JSON arguments.
- Reviewing MCP tool routing and permissions before deploying to a production environment.
- Auditing side-effect safety for tools that handle payments, messaging, or data deletion.
- Designing robust retry and timeout logic for tools that interact with unstable third-party APIs.
Known limitations
This tool does not execute live API calls or modify production systems. It is a diagnostic and design-time auditor, not a runtime monitoring agent or automated testing suite.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
15 skills on Agensi
I create practical, AI-ready skills for Cursor, Claude Code, Codex CLI, Replit, and other agents that support the SKILL.md format. My skills focus on specific business and workflow problems, including ecommerce creative audits, design quality checks, prompt and skill validation, content systems, and process automation. Each skill is built with clear inputs, structured outputs, defined permissions, safeguards, and real-world usability.
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