UX Laws MCP

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

No annotations are provided, so the description carries the full burden. It mentions analyzing based on a 'mental model' law but doesn't disclose behavioral traits such as whether this is a read-only analysis, if it requires specific permissions, what the output format might be, or any rate limits. The description is too high-level to inform the agent about how the tool behaves operationally.

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 relatively concise with two sentences, but the first sentence ('Un modelo comprimido...') is somewhat abstract and could be more direct. The second sentence clearly states the action and scope. It's front-loaded with the core purpose, though minor improvements in clarity could enhance efficiency.

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 of analyzing UI components across multiple platforms and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis produces, how results are returned, or any limitations. For a tool with 4 parameters and no structured output information, more context is needed to guide 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 tool description adds no additional meaning about parameters beyond what's in the schema, such as examples or usage tips. Baseline score of 3 is appropriate as 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 analyzes code or UI components according to a 'mental model' law for any platform, which gives a general purpose. However, it's vague about what 'analyze' entails (e.g., what outputs or insights are produced) and doesn't clearly differentiate from sibling tools like 'analyze_fitts_law' or 'ux_full_audit', which likely perform similar analyses for different principles or scopes.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description mentions it applies to 'ANY PLATFORM', but this doesn't help distinguish it from sibling tools that might also analyze UI components. There's no mention of prerequisites, constraints, or comparison to other analysis tools in the list.

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