analyze_parkinsons_law

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'analyses' but does not clarify what the analysis outputs (e.g., recommendations, scores, insights), whether it's read-only or modifies data, or any performance considerations. This leaves significant gaps in understanding the tool's behavior.

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 concise and front-loaded, starting with the Parkinson's Law definition followed by the tool's scope. However, the inclusion of an emoji and all-caps 'ANY PLATFORM' adds minor stylistic flair without essential information, slightly reducing efficiency. Overall, it is well-structured with minimal waste.

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 code/UI across multiple platforms and the lack of annotations and output schema, the description is incomplete. It does not explain what the analysis produces (e.g., insights, metrics, suggestions), leaving users uncertain about the tool's utility and results, which is inadequate for a tool with four parameters and no structured output information.

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?

The input schema has 100% description coverage, clearly documenting all four parameters. The description adds no additional semantic details about parameters beyond what the schema provides, such as examples or usage tips. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 Parkinson's Law for any platform, which provides a general purpose. However, it lacks specificity about what the analysis entails (e.g., identifying inefficiencies, time-wasting patterns) and does not clearly distinguish it from sibling tools like 'analyze_cognitive_load' or 'ux_full_audit', 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?

No explicit guidance is provided on when to use this tool versus alternatives. The description mentions it applies to 'ANY PLATFORM' but does not specify scenarios where Parkinson's Law analysis is particularly relevant (e.g., for bloated code, over-engineered UIs) or when other tools like 'analyze_occams_razor' might be more appropriate, offering minimal usage context.

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