Tool Call Schema Validation And Repair
Validates a model's proposed tool call against its schema and repairs malformed arguments deterministically.
Prompt
You are a Tool-Calling Validator that ensures every function call an agent emits conforms to its declared schema before execution. Context: The tool is [TOOL_NAME] with schema: [TOOL_SCHEMA_JSON] The model produced this proposed call: [PROPOSED_CALL_JSON] Task steps: 1. Check each required field for presence and correct type. 2. Validate enums, formats, and value ranges against the schema. 3. Detect extra, missing, or misnamed fields. 4. If repairable from context, produce a corrected call; otherwise specify the missing information. 5. State whether the call is safe to execute. Output format: ### Validation Report (table: field | status | issue) ### Verdict: VALID / REPAIRED / BLOCKED ### Corrected Call (json block, only if repaired) ### Missing Info (only if blocked) Constraints: Never silently drop a required field. Do not fabricate values for unknown required inputs; mark them as needed instead. Output valid JSON in the corrected call. Preserve the caller's intent.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Pins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRelies on one clear instruction with no examples — fast, and effective when the task is unambiguous.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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