Tool & function calling
AuraScore 93/100

Multimodal Function Call Payload Validation Checklist

Verify multimodal tool-calling payload schemas and image data routing before triggering generative diffusion endpoints.

Use this checklist when designing or auditing JSON schemas for vision-language models executing image generation tools. It ensures image buffers, parameter constraints, and fallback endpoints are strictly validated.

Template

Role: Senior Multimodal Integration Architect

Context

  • Multimodal Model: {{multimodal_model_name}}
  • Target Diffusion Endpoint: {{target_diffusion_endpoint}}
  • Image Payload Format: {{image_payload_type}}
  • Supported Aspect Ratios: {{allowed_aspect_ratios}}
  • Max Parameter Token Budget: {{max_token_budget}}
  • Fallback Failure Tool: {{error_fallback_tool}}

Task

Generate a rigorous pre-execution audit checklist to ensure {{multimodal_model_name}} correctly structures function call arguments when dispatching image synthesis requests to {{target_diffusion_endpoint}} without payload malformation.

Method

  1. Inspect parameter schema bounds for prompt strings, negative prompts, and dimension tuples.
  2. Verify {{image_payload_type}} encoding standards and binary transfer mechanisms within the tool arguments.
  3. Validate aspect ratio parsing logic against permissible values in {{allowed_aspect_ratios}}.
  4. Check token length limits against {{max_token_budget}} to prevent truncated tool arguments.
  5. Confirm strict type-checking rules for integer seeds, guidance scale floats, and scheduler enums.
  6. Evaluate error interception routing to {{error_fallback_tool}} when generation endpoints reject payloads.
  7. Review response parsers for returning generated asset URIs back into the agent context window.

Constraints

  • Checklists MUST use categorized checkboxes ([ ]) with verification criteria.
  • You MUST NOT approve ambiguous type definitions such as unconstrained string types for numeric fields.
  • All checks must specifically address multimodal tool invocation risks.
  • Output must focus purely on schema validation, payload safety, and execution boundaries.

Output format

Provide the review as a structured markdown checklist with exactly 4 sections:

  1. Argument Schema & Type Validation (4-5 check items)
  2. Payload & Encoding Integrity (3-4 check items)
  3. Parameter Range & Constraint Verification (3-4 check items)
  4. Error Interception & Fallback Routing (3-4 check items)

Self-review

  • Are all {{allowed_aspect_ratios}} and {{image_payload_type}} nuances represented?
  • Are fallback rules for {{error_fallback_tool}} clearly testable?
  • Is every item formatted as a discrete, actionable checkbox?
AuraScore breakdown
93/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification14/14 · Strong

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency7/10 · Adequate

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

ai-agents
agents-tool-calling
image-multimodal-prompting
function calling
multimodal
schema validation