analyze_goal_gradient

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool analyzes code/UI components but doesn't describe what the analysis produces, whether it's a report, score, or recommendations. It doesn't disclose computational requirements, rate limits, or what happens when analysis fails. The description is too vague about the tool's actual behavior beyond the high-level purpose.

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 reasonably concise with two sentences, but the first sentence is more of a definition of the Goal-Gradient Effect rather than a direct description of the tool's function. The structure could be improved by front-loading the tool's purpose more clearly. The emoji adds visual appeal but doesn't contribute functional information.

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?

For a 4-parameter analysis tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the analysis produces, what format the results take, or what constitutes a good vs. bad analysis. The mention of 'CUALQUIER PLATAFORMA' (any platform) is helpful context, but overall the description leaves too many behavioral questions unanswered.

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 4 parameters thoroughly. The description mentions analyzing code/UI components for any platform, which aligns with the 'code' and 'platform' parameters but doesn't add meaningful semantic context beyond what the schema provides. The baseline of 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 clearly states the tool analyzes code or UI components according to the Goal-Gradient Effect law for any platform. It specifies the verb 'analiza' (analyzes) and the resource 'código o componentes UI' (code or UI components), and mentions the psychological principle being applied. However, it doesn't explicitly differentiate from sibling tools that analyze other UX laws.

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 provides minimal guidance on when to use this tool - only stating it analyzes according to the Goal-Gradient Effect. It doesn't explain when this analysis would be appropriate versus other sibling tools like analyze_fitts_law or analyze_hicks_law, nor does it mention prerequisites or exclusions for its use.

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