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Tai-DT

mcp-ui-expo-tamagui

by Tai-DT

optimize_component

Optimize existing React Native/Expo component code by applying AI suggestions tailored to your target platform and optimization goals such as performance, accessibility, and reusability.

Instructions

Optimize existing component code with AI suggestions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesExisting component code to optimize
targetPlatformNoTarget platform for optimization
optimizationGoalsNoOptimization goals
Behavior2/5

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It says 'optimize with AI suggestions' but does not clarify whether the tool directly modifies the code, returns a diff, or only provides suggestions. It also lacks details on side effects, required permissions, or output format.

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 a single, concise sentence with no redundancy or filler. It front-loads the key action and resource, making it easy to parse. However, it is somewhat under-specified, which is penalized in other dimensions.

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

Completeness2/5

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

Without an output schema, the description should indicate what the tool returns (e.g., optimized code, suggestion list, explanation). It does not. It also lacks context about how the tool handles different scenarios, making it insufficient for an agent to fully anticipate the tool's behavior.

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 all three parameters with descriptions (100% coverage), so the baseline is 3. The description itself adds no extra meaning to the parameters, such as how targetPlatform or optimizationGoals influence the optimization process.

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

Purpose4/5

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

The description clearly states the action ('optimize') and resource ('existing component code'), identifying the tool's core purpose. It implicitly distinguishes from generate_ui_component (generation vs. optimization) but does not explicitly differentiate from the closely related sibling get_component_suggestions, so it's not a 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool instead of siblings like get_component_suggestions, generate_ui_component, or search_expo_docs. There is no mention of use cases, prerequisites, or exclusions.

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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