Tool-Use Justification in Reasoning Trace Prompt

This prompt is designed for agent evaluation teams who need to programmatically grade whether an AI agent's reasoning trace provides a valid, sufficient, and non-superfluous justification for every tool call it makes. The core job-to-be-done is moving beyond binary tool-use correctness (did it call the right API?) to auditing the agent's decision-making transparency. You should use this prompt when you are building an evaluation harness for an agent that uses external tools (APIs, databases, MCP servers) and you need to ensure its internal monologue actually explains why a tool is needed, what it expects to get back, and how the result will advance the task. The ideal user is an AI engineer or evaluation lead who already has a dataset of agent traces with tool calls and needs to add a 'justification quality' score to their existing eval pipeline.

Required context for this prompt includes: the full reasoning trace leading up to the tool call, the tool's name and schema (parameters and description), the arguments passed, and the broader task context. Without the reasoning trace, the judge has nothing to evaluate. Without the tool schema, the judge cannot assess whether the justification correctly anticipates the tool's function. Do not use this prompt for evaluating the correctness of the tool's output, the final answer quality, or the agent's overall task success—those are separate evaluation surfaces. This prompt is also inappropriate for agents that do not expose a reasoning trace, or for simple classification/routing models where tool use is a single-step decision without a chain-of-thought.

The primary constraint is that this prompt produces a structured justification quality score with specific flags, not a free-text critique. The output must be machine-readable to be wired into a CI/CD pipeline or an evaluation dashboard. The prompt is designed to catch three critical failure modes: missing rationale (the agent calls a tool with no explanation), tool-call mismatches (the justification describes one action but the arguments do something else), and unnecessary tool invocation (the agent calls a tool when the information is already present in the context or could be derived without an external call). Before integrating this prompt, ensure your evaluation dataset includes examples of each failure mode so you can calibrate the judge's sensitivity. If your agent operates in a high-stakes domain (finance, healthcare, legal), you must pair this automated evaluation with human spot-checks on a sample of flagged traces to prevent subtle justification failures from becoming systemic trust issues.

The following prompt template evaluates an agent's reasoning trace to determine whether each tool call is adequately justified. It checks for missing rationale, mismatches between stated intent and the tool actually called, and unnecessary tool invocations that could have been handled through reasoning alone. The template is designed to be dropped into an evaluation harness that feeds it agent traces and receives structured justification scores in return.

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You are an evaluation judge grading the quality of tool-use justification in an agent reasoning trace. Your job is to assess whether each tool call in the trace is properly justified, not whether the tool call itself was correct.

## INPUT

[AGENT_REASONING_TRACE]

## AVAILABLE TOOLS

[TOOL_DEFINITIONS]

## EVALUATION CRITERIA

For each tool call in the trace, evaluate the following dimensions:

1. **Rationale Presence**: Does the reasoning trace contain an explicit justification for why this tool is being called before the call occurs? Score as PRESENT, PARTIAL, or ABSENT.

2. **Rationale-Tool Alignment**: Does the stated justification actually match the tool being called? Score as ALIGNED, MISALIGNED, or UNCLEAR. Flag MISALIGNED cases where the agent says it will call one tool but calls another, or where the justification describes an action the selected tool cannot perform.

3. **Necessity Assessment**: Is the tool call necessary given the stated goal and available information? Score as NECESSARY, REDUNDANT (same information already available), PREMATURE (could have reasoned further first), or UNNECESSARY (task achievable without any tool).

4. **Argument Justification**: Are the arguments passed to the tool explained in the reasoning trace? Score as JUSTIFIED (each argument's origin explained), PARTIAL (some arguments unexplained), or UNEXPLAINED (arguments appear without reasoning).

## OUTPUT SCHEMA

Return a JSON object with this exact structure:

{
  "trace_id": "string identifier from input",
  "overall_justification_score": number (1-5 scale where 1=no justification, 5=excellent justification throughout),
  "tool_call_evaluations": [
    {
      "call_index": number (position in trace, 0-based),
      "tool_name": "string",
      "rationale_presence": "PRESENT" | "PARTIAL" | "ABSENT",
      "rationale_tool_alignment": "ALIGNED" | "MISALIGNED" | "UNCLEAR",
      "necessity": "NECESSARY" | "REDUNDANT" | "PREMATURE" | "UNNECESSARY",
      "argument_justification": "JUSTIFIED" | "PARTIAL" | "UNEXPLAINED",
      "flags": ["string descriptions of specific issues found"],
      "justification_excerpt": "string quoting the relevant justification text, or 'No justification found'"
    }
  ],
  "summary_findings": {
    "total_tool_calls": number,
    "missing_rationale_count": number,
    "misaligned_count": number,
    "unnecessary_count": number,
    "critical_flags": ["string descriptions of issues requiring human review"]
  }
}

## SCORING RUBRIC

- **5**: Every tool call has clear, specific justification before invocation. All justifications align with the tool called. No unnecessary calls. All arguments explained.
- **4**: Most tool calls well-justified. Minor issues like slightly vague rationale or one partially explained argument.
- **3**: Mixed quality. Some calls well-justified, others missing rationale or have alignment issues. At least one unnecessary or redundant call.
- **2**: Majority of calls poorly justified. Multiple missing rationales, misalignments, or unnecessary invocations.
- **1**: No meaningful justification present. Tool calls appear arbitrary or completely disconnected from reasoning.

## CONSTRAINTS

- Evaluate justification quality only. Do not judge whether the tool call achieved the right outcome.
- If the trace contains no tool calls, return overall_justification_score as null and empty tool_call_evaluations array.
- Quote justification excerpts verbatim from the trace. Do not paraphrase.
- Flag for human review when: MISALIGNED calls, UNNECESSARY calls that could cause side effects, or any case where the agent's reasoning contradicts the tool call made.
- [ADDITIONAL_CONSTRAINTS]

To adapt this template for your evaluation pipeline, replace [AGENT_REASONING_TRACE] with the actual trace text from your agent runs. Populate [TOOL_DEFINITIONS] with your tool schemas so the judge can verify alignment between stated intent and tool capabilities. The [ADDITIONAL_CONSTRAINTS] placeholder lets you add domain-specific rules, such as requiring justification for write operations but not read operations, or flagging tool calls that access sensitive data without explicit rationale. Wire this prompt into an evaluation harness that parses the JSON output and routes critical flags to a human review queue. For high-stakes agent workflows where unjustified tool calls could cause financial or operational damage, run this evaluation before allowing the agent to execute tool calls in production.

This prompt is designed to operate as a model-graded evaluation step within a larger agent observability pipeline, not as a standalone chat interaction. The primary integration point is after an agent has produced a reasoning trace that includes one or more tool calls. Your application should extract the full reasoning trace and the list of tool invocations (with their arguments and results) from your agent execution logs or trace store. Feed these into the prompt as [REASONING_TRACE] and [TOOL_CALLS]. The prompt returns a structured JSON justification quality score and a list of per-call flags, which your harness should parse and log alongside the original trace for downstream analysis.

Validation and retry logic is critical because the output schema includes enum fields (justification_quality, flag_severity) and structured arrays. Implement a JSON schema validator in your harness that checks: (1) the top-level justification_quality field is present and matches one of the allowed enum values (adequate, partial, missing, mismatched), (2) the flags array contains objects with all required fields (tool_call_index, flag_type, severity, explanation), and (3) tool_call_index values are integers that correspond to actual tool calls in the input. If validation fails, retry once with the validation error message appended to the prompt as a [PREVIOUS_OUTPUT_ERROR] constraint. If the second attempt also fails validation, log the raw output and escalate to a human review queue rather than silently accepting a malformed evaluation.

Model choice and latency considerations matter here. This evaluation prompt requires careful reading of potentially long reasoning traces and cross-referencing them against tool call arguments. Use a model with strong instruction-following and long-context reasoning capabilities (such as Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro). Avoid smaller or faster models that may hallucinate flag details or miss subtle justification mismatches. Because this is an offline evaluation step rather than a real-time user-facing interaction, latency tolerance is higher—batch processing of traces is acceptable. However, if you are evaluating traces at high volume, consider implementing a pre-filter that only runs this prompt on traces where tool calls were actually made, skipping traces with zero tool invocations to reduce cost.

Human review integration should be triggered by specific severity thresholds. Configure your harness to automatically escalate any trace where the justification_quality score is missing or mismatched, or where any individual flag has a severity of critical. These cases indicate that the agent either failed to explain why it used a tool or used a tool that contradicts its own reasoning—both are high-risk failure modes that warrant human inspection before the agent's behavior is considered acceptable. For partial scores with only low severity flags, you may choose to aggregate these into a weekly review batch rather than triggering immediate alerts.

Logging and observability should capture the full evaluation payload alongside the original trace. Store the justification quality score, the flag array, the model used for evaluation, the timestamp, and the trace ID in your evaluation database. This enables longitudinal analysis: you can track whether justification quality is improving or degrading across agent versions, identify which tool types most frequently lack justification, and correlate poor justification scores with downstream task failures. Wire these metrics into your existing monitoring dashboards so that justification quality becomes a tracked evaluation dimension alongside accuracy, latency, and cost.