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fix_code

Evaluate code and automatically apply fixes to resolve security, performance, and quality issues.

Instructions

Evaluate code with the Judges Panel and automatically apply all available auto-fix patches. Returns the fixed code along with a summary of applied and remaining findings. Use this to fix security, performance, and quality issues in a single step.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe source code to evaluate and fix. Include the full file content.
configNoOptional inline configuration (same format as .judgesrc)
contextNoOptional additional context about the code — e.g., what the code does, which framework it uses.
judgeIdNoOptional: restrict fixes to a single judge. One of: accessibility, agent-instructions, ai-code-safety, api-contract, api-design, authentication, backwards-compatibility, caching, ci-cd, cloud-readiness, code-structure, compliance, concurrency, configuration-management, cost-effectiveness, cybersecurity, data-security, data-sovereignty, database, dependency-health, documentation, error-handling, ethics-bias, framework-safety, hallucination-detection, iac-security, intent-alignment, internationalization, logging-privacy, logic-review, maintainability, model-fingerprint, multi-turn-coherence, observability, over-engineering, performance, portability, rate-limiting, reliability, scalability, security, software-practices, testing, ux, false-positive-review
languageYesThe programming language of the code (e.g., 'typescript', 'python', 'javascript', 'csharp', 'java').
minConfidenceNoMinimum finding confidence to include fixes for (0-1, default: 0.5)
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. While it states the tool evaluates and applies fixes, it fails to disclose important behavioral traits: whether it is destructive (modifies input), idempotent, what the 'Judges Panel' entails, error handling, or any side effects. For a mutation tool, this is insufficient transparency.

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

Conciseness5/5

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

The description is exceptionally concise (two sentences) and front-loads the core action ('evaluate ... and automatically apply ... patches'). Every sentence is functional with zero redundancy.

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?

For a tool with 6 parameters, full schema coverage, and no output schema, the description provides a high-level overview but lacks details on output format, behavior when no fixes exist, or error conditions. It is adequate but not comprehensive, especially given the absence of annotations.

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 description coverage is 100%, so baseline is 3. The description adds minimal context beyond schema descriptions (e.g., 'same format as .judgesrc' for config), but does not significantly enhance understanding of parameters. It meets the minimal bar without adding extra value.

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 it evaluates code and applies auto-fix patches, returning fixed code and a summary. It uses strong verbs ('evaluate', 'apply', 'returns') and specifies the resource ('code'). It distinguishes itself from sibling evaluation-only tools like 'evaluate_code' by explicitly mentioning automatic fix application and returning fixed code.

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

Usage Guidelines3/5

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

The description suggests using this tool to fix issues in a single step, but does not explicitly guide when to use alternatives (e.g., 'evaluate_code' for evaluation only, 'evaluate_then_fix' for a two-step process). The usage is implied rather than explicitly stated with when/when-not conditions.

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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