Argument Confidence Scoring Prompt for Tool Calls

This prompt is for integration developers and safety engineers who need to validate the quality of tool call arguments before execution. The job-to-be-done is scoring each argument in a proposed tool call for correctness, completeness, and consistency with the user's intent and available context. The ideal user is someone building a pre-execution guardrail layer—they already have a tool selected and arguments populated, but they need a calibrated confidence signal per argument to decide whether to proceed, request clarification, or escalate for human review.

Use this prompt when the cost of executing a tool call with wrong or incomplete arguments is high—for example, write operations that mutate state, financial transactions, clinical data entry, or customer-facing actions. The prompt requires the proposed tool name, the populated arguments, the original user request, and any relevant conversation or document context. It is not designed for tool selection decisions (use the Tool Selection Confidence Scoring Prompt for that) or for deciding whether to call a tool at all (use the Low-Confidence Tool Call Abstention Prompt). It assumes a tool has already been chosen and arguments have been filled; its sole job is per-argument quality assessment.

Do not use this prompt for low-risk read operations where argument errors are easily detectable and reversible. Do not use it as a substitute for schema validation or type checking—those should happen in the application layer before this prompt runs. The prompt works best as part of a staged pipeline: schema validation first, then argument confidence scoring, then a threshold-based gate that routes high-confidence calls to execution, medium-confidence to a clarification loop, and low-confidence to human review. Pair this with the Confidence Threshold Tuning Prompt for Production Deployments to calibrate your thresholds from historical outcomes.

This prompt template is designed to be inserted into your tool-calling pipeline after the model has selected a tool and constructed its arguments, but before those arguments are sent to the tool for execution. It instructs the model to evaluate each argument independently, assign a confidence score, and flag any argument that falls below a configurable threshold. The output is a structured assessment that your application can use to decide whether to proceed, request clarification, or escalate for human review.

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You are an argument confidence scorer for tool calls. Your job is to evaluate each argument in a proposed tool call and assign a confidence score from 0.0 to 1.0 for each one.

## INPUT
Tool Name: [TOOL_NAME]
Tool Description: [TOOL_DESCRIPTION]
Proposed Arguments: [ARGUMENTS_JSON]
Conversation Context: [CONVERSATION_CONTEXT]
Available Evidence: [EVIDENCE_SOURCES]

## SCORING CRITERIA
For each argument, assess:
1. **Source Grounding**: Is the argument value directly supported by the conversation context or evidence sources? (0.0 = hallucinated, 1.0 = explicitly stated)
2. **Type Correctness**: Does the value match the expected type and format for this parameter? (0.0 = wrong type, 1.0 = perfectly conforms)
3. **Semantic Fit**: Does the value make sense for the tool's purpose and the user's intent? (0.0 = nonsensical, 1.0 = clearly appropriate)
4. **Completeness**: Is the value fully specified, or is it truncated, vague, or missing required sub-fields? (0.0 = critically incomplete, 1.0 = fully specified)

## OUTPUT SCHEMA
Return a JSON object with this exact structure:
{
  "tool_name": "string",
  "overall_confidence": 0.0,
  "arguments": [
    {
      "parameter_name": "string",
      "provided_value": "any",
      "confidence_score": 0.0,
      "flags": ["string"],
      "reasoning": "string"
    }
  ],
  "arguments_below_threshold": ["string"],
  "recommendation": "proceed" | "clarify" | "escalate",
  "recommendation_reason": "string"
}

## CONSTRAINTS
- Score each argument independently. Do not let one argument's confidence influence another's.
- If an argument's value is missing, null, or an empty string, assign a confidence_score of 0.0 and add the flag "MISSING_REQUIRED".
- If an argument's value appears to be a guess or inference without direct evidence, assign a confidence_score below 0.5 and add the flag "INFERRED".
- If an argument's value contradicts available evidence, assign a confidence_score of 0.0 and add the flag "CONTRADICTS_EVIDENCE".
- The overall_confidence is the minimum of all argument confidence scores.
- Set recommendation to "proceed" only if all arguments are at or above [CONFIDENCE_THRESHOLD].
- Set recommendation to "clarify" if any argument is below [CONFIDENCE_THRESHOLD] but the missing information could reasonably be obtained from the user.
- Set recommendation to "escalate" if any argument is below [CONFIDENCE_THRESHOLD] and the ambiguity requires human judgment or domain expertise.
- Do not modify the provided argument values. Only score and flag them.

## RISK LEVEL
[RISK_LEVEL]

## EXAMPLES
[EXAMPLES]

To adapt this template, replace the square-bracket placeholders with values from your application context. [TOOL_NAME] and [TOOL_DESCRIPTION] should come from your function schema definitions. [ARGUMENTS_JSON] is the raw arguments object the model produced. [CONVERSATION_CONTEXT] should include the last N turns of the conversation. [EVIDENCE_SOURCES] can include retrieved documents, user profile data, or system state. [CONFIDENCE_THRESHOLD] is a float you control—start at 0.7 and tune based on your eval results. [RISK_LEVEL] should be a short string like "HIGH - financial transaction" or "MEDIUM - CRM update" that helps the model calibrate its scoring conservatism. [EXAMPLES] should contain 2-3 few-shot examples showing correct scoring for your domain's typical argument patterns, including at least one example that results in "clarify" or "escalate".

After implementing this prompt, validate its output against a golden dataset of known argument-quality cases before deploying to production. Pay particular attention to the arguments_below_threshold field—your application harness should read this array and block execution of any tool call where it is non-empty, routing to the path indicated by the recommendation field. Do not rely on the model's overall_confidence alone; always check per-argument scores to catch single-argument failures that could corrupt downstream state.

The Argument Confidence Scoring Prompt is not a standalone module—it is a pre-execution safety layer that sits between the model's initial tool-call generation and the actual invocation of that tool. In a production harness, you should intercept the model's raw tool call, extract the function name and arguments, and pass only the arguments (along with the tool schema and user context) to this scoring prompt. The prompt returns per-argument confidence scores and flags any argument that falls below a configurable threshold. This output must be parsed into a structured object before any tool execution proceeds.

Build the harness to enforce a strict contract: if any required argument scores below the threshold, the system must not execute the tool. Instead, route the flagged arguments to one of three recovery paths. For low-risk, non-mutating tools, you may re-prompt the model with the specific flagged argument and the original context to attempt a correction (with a maximum of one retry). For medium-risk tools, surface the flagged arguments to the user for clarification before proceeding. For high-risk or write-operation tools, escalate to a human review queue with the full scoring payload, original user request, and candidate tool call. Log every scoring result—including the raw prompt response, parsed scores, threshold decision, and chosen recovery path—as a structured audit record. This log is essential for calibrating thresholds and debugging downstream errors.

Model choice matters here. Use a model with strong instruction-following and structured output capabilities for the scoring step. If your primary model is a smaller or faster one used for initial tool selection, consider routing the scoring prompt to a more capable model (or even the same model with a lower temperature and strict JSON mode). Implement a parsing validator that rejects any scoring response missing required fields (argument_name, confidence_score, below_threshold, reasoning) or containing scores outside the 0.0–1.0 range. If parsing fails, retry the scoring prompt once with an explicit error message injected into the prompt context. If it fails again, treat it as a low-confidence result and escalate to human review rather than silently proceeding.

For evaluation, maintain a golden dataset of argument sets with known-good and known-bad values. Run the scoring prompt against this dataset in your CI pipeline whenever the prompt template, threshold, or model changes. Track per-argument calibration—does a score of 0.8 actually mean the argument is correct 80% of the time? Correlate low-scoring arguments with downstream tool execution errors to tune your threshold. A threshold that is too low lets bad arguments through; too high creates unnecessary clarification friction. Start with a threshold of 0.7 for required arguments and 0.5 for optional ones, then adjust based on production data. Never deploy a threshold change without running it against your golden dataset first.