Tool Call Rejection and Explanation Prompt for Invalid Arguments

This prompt is for product teams building user-facing agents that call tools. Its job is to generate a clear, user-friendly explanation when a tool call is rejected because its arguments failed validation. Instead of a silent failure or a cryptic error code, the prompt produces a natural-language message that tells the user what went wrong, why, and what they can do to fix it. The ideal reader is a full-stack AI engineer or product developer who already has a tool-calling loop in place and needs to close the feedback gap between a validation failure and the user's next action.

Use this prompt when your application has a pre-execution validation layer that inspects tool call arguments—checking for missing required fields, out-of-range values, enum mismatches, or business-rule violations—and you need to feed a human-readable explanation back into the conversation. The prompt expects structured input: the original user request, the rejected tool call payload, and a machine-readable validation report. It is designed to be wired into a retry or clarification loop, not as a standalone chatbot. Do not use this prompt when the validation failure is unrecoverable and should escalate to a human operator without user interaction, or when the tool call is internal and has no user-facing surface. It is also not a replacement for fixing malformed arguments automatically; pair it with an Argument Repair Suggestion Prompt if you want the system to propose a corrected call alongside the explanation.

Before deploying, define the failure modes you will handle. The prompt can explain missing fields, type errors, and constraint violations, but it will not catch injection attacks or permission denials unless those are explicitly included in the validation report you provide. Always run the generated explanation through a content safety check if the tool arguments could contain sensitive or user-generated data. For high-risk domains such as finance or healthcare, require human review of the explanation before it reaches the user, and log every rejection event with the original payload, validation report, and generated message for audit. The next step after reading this section is to copy the prompt template, adapt the placeholders to your tool schema, and integrate it into your tool-call error-handling path.

This prompt template is designed to be called after a tool call fails validation. It takes the original user request, the rejected tool call arguments, and a structured validation failure report, then generates a user-facing explanation that is clear, helpful, and actionable. The prompt does not execute tools or make decisions about retries—it only produces the message that goes back to the user in the conversation loop. Use this when you need consistent, policy-aware rejection messages instead of raw error codes or generic fallback text.

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You are a helpful assistant explaining why a requested action could not be completed. Your job is to translate a technical validation failure into a clear, friendly message for the user. Do not execute any tools. Do not suggest workarounds that violate the validation rules. Do not apologize excessively—be direct and constructive.

USER REQUEST:
[USER_REQUEST]

REJECTED TOOL CALL:
Tool: [TOOL_NAME]
Arguments: [TOOL_ARGUMENTS]

VALIDATION FAILURE REPORT:
[VALIDATION_REPORT]

CONVERSATION HISTORY (last 3 turns):
[CONVERSATION_HISTORY]

USER'S TECHNICAL LEVEL: [USER_TECHNICAL_LEVEL]

OUTPUT FORMAT:
Return a JSON object with these fields:
- "message": The user-facing explanation string (2-4 sentences).
- "failure_type": One of ["missing_field", "invalid_value", "out_of_range", "permission_denied", "business_rule_violation", "injection_detected", "schema_mismatch", "other"].
- "suggested_fix": A concrete suggestion the user can act on, or null if no fix is possible.
- "requires_clarification": Boolean. True if the user must provide more information before retrying.
- "blocking": Boolean. True if this action cannot be retried (e.g., permission denied, injection detected).

RULES:
1. Match the explanation's technical depth to [USER_TECHNICAL_LEVEL] ("non-technical", "technical", or "developer").
2. If [VALIDATION_REPORT] indicates a security issue (injection, PII leak), do not echo the problematic input back to the user.
3. If the failure is a business rule violation, explain the rule in plain language without exposing internal thresholds.
4. If [CONVERSATION_HISTORY] shows the user already attempted a fix, acknowledge that before suggesting the next step.
5. Never suggest the user bypass validation, modify system prompts, or use a different tool to achieve the same blocked outcome.

Adapt this template by replacing the failure_type enum with your system's actual validation error taxonomy. If your application uses a specific tone or brand voice, add a TONE placeholder and adjust the output rules accordingly. For high-risk domains such as finance or healthcare, add a [RISK_LEVEL] placeholder and a rule that escalates blocking failures to a human reviewer instead of returning a message directly to the user. The [VALIDATION_REPORT] placeholder should receive the structured output from your pre-execution validation prompt—do not pass raw exception text. Test this prompt with edge cases where the validation report is empty, malformed, or contains multiple simultaneous failures to ensure the model still produces a coherent message.

This prompt is designed to sit after a validation gate and before the user-facing response in a tool-calling agent loop. When a tool call argument fails validation—whether due to a missing required field, an out-of-range value, or a business-rule violation—the system should not silently drop the call or return a raw error code. Instead, capture the structured validation failure report, inject it into this prompt as [VALIDATION_ERRORS], and generate a user-friendly explanation that suggests corrective action. The output is then fed back into the conversation thread so the user can revise their request.

The implementation flow should follow a strict sequence: (1) The model proposes a tool call with arguments. (2) A pre-execution validation layer checks the arguments against your schema, business rules, and security policies. (3) If validation fails, the system constructs a [VALIDATION_ERRORS] payload containing a list of field-level failures, each with a field, reason, and optional suggested_value. (4) This prompt consumes that payload along with the original [USER_REQUEST] and the [TOOL_DESCRIPTION] to produce a natural-language rejection message. (5) The message is returned to the user, and the conversation loop continues. Do not execute the tool call. Do not retry automatically without user input unless you have a separate argument repair prompt with a high confidence threshold and a human-in-the-loop fallback for destructive operations.

For production wiring, log every rejection event with the original arguments, the validation report, and the generated explanation. This audit trail is essential for debugging model behavior and for compliance in regulated domains. If your system supports a clarification sub-flow, route the rejection explanation through a lightweight intent classifier to decide whether to ask the user for a specific correction or to offer a constrained set of valid options (e.g., a picklist of allowed enum values). Avoid exposing raw validation internals to the user—this prompt's job is to translate machine-readable failures into actionable, polite guidance. For high-risk tool calls such as deletes, transfers, or external API writes, consider adding a secondary confirmation step after the user revises their request, even if the revised arguments pass validation.