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 for any platform but doesn't disclose behavioral traits such as what the analysis output includes (e.g., textual feedback, scores), whether it's a read-only operation, potential rate limits, or authentication needs. This leaves significant gaps for an agent to understand how the tool behaves.

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: it introduces the law, states the tool's purpose, and specifies platform scope. It's front-loaded with the core purpose, though the first sentence is more explanatory than directly tool-focused, slightly reducing 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the analysis returns (e.g., insights, errors, or structured data), which is critical for a tool with 4 parameters and complex platform options. This leaves the agent guessing about the tool's output and full utility.

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 well. The description adds no additional meaning about parameters beyond implying platform flexibility, which is already covered by the schema's enum. This meets the baseline for high schema coverage.

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 the Law of Uniform Connectedness, which provides a general purpose. However, it doesn't specify what the analysis produces (e.g., recommendations, scores, or insights) or how it differs from sibling tools like analyze_proximity_law or analyze_similarity_law, making it somewhat vague.

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 mentions the tool is for analyzing code or UI components for any platform, but it provides no guidance on when to use this specific law versus other sibling tools (e.g., analyze_common_region_law or analyze_proximity_law). There's no explicit context, exclusions, or alternatives mentioned.

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