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Platano78

Smart-AI-Bridge

dual_iterate

Generate high-quality code via an iterative generate-review-fix loop using dual AI backends. The internal process returns only the final approved code, minimizing token consumption for complex tasks.

Instructions

Dual Iterative Code Generation - Internal generate->review->fix loop using dual backends. Generator creates code, reviewer validates, generator fixes. Runs entirely within Smart AI Bridge, returning only final approved code to Claude. Massive token savings for complex generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesCode generation task description (e.g., "Write a function that validates email addresses")
max_iterationsNoMaximum review iterations before accepting result (default: 3)
include_historyNoInclude iteration history in response (useful for debugging)
quality_thresholdNoQuality threshold for accepting code (0.5-1.0)
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes the loop and token savings but lacks details on failure handling, auth needs, or rate limits. Schema coverage is high, but behavioral gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences efficiently convey purpose and process. Some redundancy ('using dual backends' vs 'dual backends') could be trimmed, but structure is good.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the process but could elaborate on output format and failure scenarios (e.g., what happens if quality threshold not met). No output schema exists, so description should cover more.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already well-documented. The description adds context for the overall process but does not add parameter-specific meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: an internal generate->review->fix loop using dual backends. It distinguishes from siblings like 'generate_file' by emphasizing the iterative internal process and token savings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for efficient complex code generation, stating it runs internally and returns only final approved code. However, it does not explicitly state when not to use or provide alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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