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Generate gated code

generate_code
Read-only

Generate code with automatic validation gating, retrying rejected attempts and clearly marking unvalidated results.

Instructions

Generate code and gate it in one step: an LLM writes the implementation, then the same protection engine as validate_ai_output vets it — retrying generation when the gate rejects. If every attempt is vetoed you still receive the last attempt, clearly marked validated:false with the gate findings — rejected code is never presented as clean. Generation runs on our infrastructure; nothing executes in your environment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesWhat to build, with any constraints worth enforcing — e.g. 'a rate-limited retry decorator with exponential backoff, stdlib only'.
languageNoTarget language for the generated code, e.g. 'python' (default), 'typescript', 'go', 'sql'.python
max_tokensNoGeneration budget. The default 4000 fits most functions/classes; raise it for multi-file scaffolds.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoThe generated implementation
attemptsNoGeneration attempts before approval or give-up
providerNo
generatedYesFalse only when no provider returned code
validatedNoTrue if the protection gates approved the final attempt; false means the gate findings in 'protection' explain the veto
protectionNoDeterministic gate result. A veto here is final — the model review cannot override it.
validation_scoreNoDeterministic gate score, 0-100
Behavior5/5

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

The description discloses key behaviors beyond annotations: automatic retries on gate rejection, returning the last attempt marked validated:false with findings if all attempts are vetoed, and that generation runs on infrastructure without executing in the user's environment. This richly complements the readOnlyHint annotation.

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?

The description is three sentences long but each sentence carries substantive information: the process, the failure behavior, and the execution environment. There is no redundant structure, though it is slightly dense.

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

Completeness5/5

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

The description addresses the most important contextual factors: what happens on repeated rejections, the safety of the execution model, and the relation to validate_ai_output. Given the presence of an output schema, it does not need to detail return values.

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?

The input schema already provides 100% coverage for all three parameters with detailed descriptions. The description adds no additional parameter-specific meaning beyond stating the generation context, so a baseline score is appropriate.

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 opens with 'Generate code and gate it in one step', clearly stating the tool's combined generation and validation function. It explicitly differentiates from sibling validate_ai_output by referencing the same protection engine, making the scope unambiguous.

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 this tool is for generating code with built-in validation, and mentions validate_ai_output as the source of the protection engine, suggesting that tool handles validation alone. This gives clear context, though it doesn't explicitly list when not to use 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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