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plan_ui_fixes

Generate a dry-run UI fix plan from diagnosis evidence and app graph data, including patch risk, confidence, and affected files, to inform manual or automated fixes without modifying source code.

Instructions

Build a dry-run UI fix plan from diagnosis evidence and app graph data.

Returns on success: { patches[], summary, caveats[] } where every patch includes risk, confidence, affected files, operations, and writeSafe. This tool never modifies source files.

Use this tool: to decide what a human or coding agent should patch before calling memi fix apply or making manual edits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNoLocal path or public URL to scan. Defaults to the current project root.
maxFilesNoMaximum source files to scan.
Behavior5/5

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

With no annotations, the description fully discloses that the tool never modifies source files, is a dry-run, and returns structured patch information. It is transparent about its safety and behavior.

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 three sentences plus a usage directive, all essential. It front-loads purpose, returns, and safety, with no wasted words.

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

Completeness4/5

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

Despite no output schema, the description lists return fields and patch structure. It could mention prerequisites (whether diagnosis evidence is required), but overall provides sufficient context for an agent to decide usage.

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 covers 100% of parameters with descriptions. The tool description does not add additional meaning beyond what the schema already provides, meeting the baseline for high coverage.

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 builds a dry-run UI fix plan from diagnosis evidence and app graph data, with a specific verb and resource. It differentiates from siblings by emphasizing read-only planning, and it describes the return structure.

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 explicitly says to use it to decide patches before applying fixes with memi fix apply or manual edits. It implies when not to use by stating it never modifies files, but it does not explicitly list alternative tools.

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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