Tool Use Error Categorization and Root Cause Prompt

This prompt is built for agent platform teams who have moved beyond simple pass/fail tool-use metrics and need to understand why their agents are failing. The core job-to-be-done is offline root-cause analysis: you have a batch of agent traces with known failures, and you need to prioritize which failure class to fix first. The prompt classifies every tool-use error into a structured taxonomy—wrong tool, bad arguments, timing error, hallucination, misinterpretation—and attributes a root cause. The output is a categorized error report with frequency distributions, designed to feed directly into sprint planning and model improvement backlogs.

Use this prompt when you are conducting a post-mortem on a test suite run, analyzing production traces in a debugging session, or comparing failure profiles across model versions. It is ideal for generating a prioritized backlog of agent improvements. Do not use this prompt for real-time agent decision-making or as a guardrail during live execution. It is an offline evaluation and diagnosis tool, not a runtime classifier. It also assumes you have already identified which traces contain failures; it does not perform the initial pass/fail grading. Pair it with a Tool Call Pass/Fail Gate Criteria Prompt to filter traces first, then feed the failures into this prompt for categorization.

Before using this prompt, ensure you have structured trace data including the agent's reasoning, the tool catalog provided, the tool calls made, their arguments, and the outcomes. The prompt's value scales with trace volume—single-trace analysis is better served by manual inspection. For safety-critical tool-use workflows, always route the final categorized report through a human review step before committing to remediation priorities. The taxonomy itself should be treated as a living artifact; if your agent's failure modes evolve, update the taxonomy in the prompt to prevent drift between what you measure and what actually breaks in production.

This prompt template is designed to be pasted directly into your evaluation harness. It takes a tool-use trace, the agent's context, and your tool catalog as input, then produces a structured error categorization report. The template uses square-bracket placeholders that you must replace with real data before sending to the model. Every placeholder is documented so you can wire this into an automated pipeline without guessing.

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You are an expert evaluator of AI agent tool-use behavior. Your task is to analyze a tool-use trace and classify any failures into a predefined error taxonomy, then attribute root causes.

## INPUTS

### Tool Catalog
[TOOL_CATALOG]

### Agent Context
[AGENT_CONTEXT]

### Tool-Use Trace
[TOOL_TRACE]

### Expected Behavior (Optional)
[EXPECTED_BEHAVIOR]

## ERROR TAXONOMY

Classify each failure into exactly one of these categories:

1. **WRONG_TOOL**: The agent selected a tool that is inappropriate for the task, not in the catalog, or clearly suboptimal given available alternatives.
2. **BAD_ARGUMENTS**: The correct tool was selected, but one or more arguments are incorrect, missing required fields, have wrong types, or contain hallucinated values.
3. **TIMING_ERROR**: The tool was called too early (before necessary context was gathered) or too late (after sufficient information was already available).
4. **HALLUCINATION**: The agent invented a tool name, parameter, or capability not present in the provided tool catalog.
5. **MISINTERPRETATION**: The agent misunderstood the tool's purpose, output format, or side effects, leading to incorrect usage despite correct syntax.
6. **REDUNDANT_CALL**: The tool call was unnecessary because the information was already available or a prior call already produced the needed result.
7. **MISSING_CALL**: The agent should have called a tool but did not, either by abstaining incorrectly or by proceeding without required information.
8. **SEQUENCE_ERROR**: Multi-step tool calls were executed in the wrong order, violating dependency constraints.
9. **SAFETY_VIOLATION**: The tool call attempted an unauthorized action, scope escalation, or dangerous parameter combination.
10. **RESULT_MISUSE**: The agent received a correct tool result but misinterpreted, ignored, or incorrectly applied the output in subsequent reasoning.

## OUTPUT SCHEMA

Return a valid JSON object with this exact structure:

{
  "summary": {
    "total_calls": <integer>,
    "failed_calls": <integer>,
    "pass_rate": <float between 0 and 1>,
    "dominant_failure_category": <string or null>
  },
  "failures": [
    {
      "call_index": <integer, 0-based position in trace>,
      "tool_name": <string>,
      "error_category": <one of the taxonomy values above>,
      "severity": <"CRITICAL" | "MAJOR" | "MINOR">,
      "description": <string, concise explanation of what went wrong>,
      "root_cause": <string, attribution to one of: "INSTRUCTION_GAP" | "CONTEXT_INSUFFICIENCY" | "TOOL_DESCRIPTION_AMBIGUITY" | "MODEL_CAPABILITY_LIMIT" | "PROMPT_CONFLICT" | "UNKNOWN">,
      "evidence": <string, direct quote or reference from the trace or context>,
      "suggested_fix": <string, actionable recommendation>
    }
  ],
  "frequency_distribution": {
    "WRONG_TOOL": <integer>,
    "BAD_ARGUMENTS": <integer>,
    "TIMING_ERROR": <integer>,
    "HALLUCINATION": <integer>,
    "MISINTERPRETATION": <integer>,
    "REDUNDANT_CALL": <integer>,
    "MISSING_CALL": <integer>,
    "SEQUENCE_ERROR": <integer>,
    "SAFETY_VIOLATION": <integer>,
    "RESULT_MISUSE": <integer>
  },
  "root_cause_attribution": {
    "INSTRUCTION_GAP": <integer>,
    "CONTEXT_INSUFFICIENCY": <integer>,
    "TOOL_DESCRIPTION_AMBIGUITY": <integer>,
    "MODEL_CAPABILITY_LIMIT": <integer>,
    "PROMPT_CONFLICT": <integer>,
    "UNKNOWN": <integer>
  },
  "improvement_priorities": [
    {
      "category": <string>,
      "impact": <"HIGH" | "MEDIUM" | "LOW">,
      "rationale": <string>
    }
  ]
}

## CONSTRAINTS

- Only classify actual failures. Do not flag correct tool calls as errors.
- If [EXPECTED_BEHAVIOR] is provided, use it as ground truth for determining correctness.
- If a call has multiple failure modes, choose the most impactful category.
- Set severity to CRITICAL if the failure would cause downstream system breakage, data corruption, or safety violations.
- Set severity to MAJOR if the failure produces incorrect results but does not break downstream systems.
- Set severity to MINOR if the failure is suboptimal but still produces usable results.
- For root_cause, prefer specific attributions over UNKNOWN. Use UNKNOWN only when no clear cause is identifiable.
- Base all evidence fields on direct quotes or explicit references from the trace or context.
- Do not invent failures that are not present in the trace.

## EVALUATION INSTRUCTIONS

1. Parse the tool trace to identify each individual tool call.
2. For each call, determine if it was correct given the agent context and expected behavior.
3. For incorrect calls, classify the error using the taxonomy above.
4. Attribute a root cause to each failure.
5. Compute the frequency distribution across all failures.
6. Generate improvement priorities based on failure impact and frequency.
7. Return the complete JSON object as specified.

To adapt this template for your pipeline, replace each placeholder with live data. [TOOL_CATALOG] should contain your full tool definitions including names, descriptions, parameter schemas, and any usage constraints. [AGENT_CONTEXT] should include the user's request, system prompt, conversation history, and any retrieved context the agent had access to. [TOOL_TRACE] should be the complete sequence of tool calls with their arguments and returned results. [EXPECTED_BEHAVIOR] is optional but strongly recommended for regression testing—it should describe the correct tool-use sequence so the evaluator can compare actual behavior against ground truth. If you omit expected behavior, the evaluator will rely on its own judgment of correctness, which reduces reliability for gating decisions.

Before deploying this prompt in a CI/CD gate, validate that your tool catalog format is consistent across test cases. Inconsistent tool descriptions will cause the evaluator to flag MISINTERPRETATION errors that are actually data formatting issues. Run this prompt against a golden dataset of 20-50 annotated traces where you know the correct error categorizations, and measure inter-rater agreement between the LLM judge and your human annotations. If agreement drops below 0.8 on critical failure categories, calibrate by adding few-shot examples to the prompt or refining your taxonomy definitions. For high-risk agent workflows where tool misuse could cause financial transactions or data deletion, always route CRITICAL severity findings to human review before accepting the automated categorization.

This prompt is designed to run as a post-execution analysis step, not in the hot path of an agent. After an agent trace completes (or fails), feed the full trace—including user input, reasoning steps, tool calls, tool responses, and final output—into this prompt to produce a structured error report. The prompt expects a complete trace log and a reference tool catalog so it can distinguish between correct tool use and failures. Run this prompt asynchronously after agent execution, not synchronously during it, because the analysis requires the full trace to categorize errors and attribute root causes.

Wire the prompt into an evaluation harness that calls your LLM API with the trace and tool catalog as inputs. Validation is critical: parse the JSON output and verify that every error category in the error_categories array matches your predefined taxonomy. Reject any output that introduces novel categories or omits the root_cause_attribution field. Implement a retry loop with up to 3 attempts if validation fails, appending the validation error message to the next request so the model can self-correct. Log every attempt, the raw output, and the validation result for later audit. For high-stakes agent workflows—such as those involving financial transactions, healthcare data, or safety-critical actions—add a human review gate that queues categorized error reports with severity: "critical" or confidence_below: 0.8 for manual inspection before the report is used to prioritize engineering work.

Choose a model with strong structured output capabilities and a context window large enough to hold your longest agent traces plus the tool catalog. GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro are suitable defaults. If you're processing traces at high volume, consider batching multiple traces into a single request with clear delimiters and asking for an array of reports to reduce API overhead. Store the categorized error reports in your observability platform alongside the original traces so you can track error frequency distributions over time and measure whether prompt or agent changes reduce specific error categories. Avoid running this prompt on incomplete or truncated traces—if the trace is cut off, skip analysis and flag it for trace buffer size review instead of producing a misleading error report.