UX Laws MCP

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions analyzing code/UI components for the Von Restorff effect but doesn't describe what the tool does behaviorally (e.g., returns insights, scores, suggestions), whether it's read-only or mutative, or any constraints like rate limits or authentication needs. This leaves significant gaps in understanding how the tool operates.

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

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

The description is appropriately sized with three sentences: an explanation of the Von Restorff effect, the tool's purpose, and platform scope. It's front-loaded with the effect definition, but the second sentence could be more direct. There's minimal waste, though the emoji and capitalization are stylistic but not detrimental.

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

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

Given the complexity (analyzing UI/code for a psychological effect), no annotations, and no output schema, the description is incomplete. It doesn't explain what the analysis entails, what output to expect, or how to interpret results. For a tool with 4 parameters and behavioral uncertainty, this lacks sufficient context for effective use.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (code, component_description, platform, context) with descriptions. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description states the tool 'analiza código o componentes UI según esta ley' (analyzes code or UI components according to this law), which provides a general purpose. However, it's vague about what specific analysis it performs (e.g., detection, evaluation, recommendations) and doesn't clearly distinguish from siblings like 'analyze_similarity_law' or 'analyze_uniform_connectedness', which also analyze UI laws. The title is null, so the description carries the full burden.

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

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

The description implies usage by specifying it analyzes 'CUALQUIER PLATAFORMA' (ANY PLATFORM) with examples, giving some context. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., vs. 'analyze_similarity_law' for different principles) or provide exclusions. The guidance is implied rather than explicit.

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