smart-validate

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

The description discloses several behavioral traits: it groups similar issues, prioritizes by impact, suggests fixes, detects anti-patterns, and flags suspicious quantifiers. It includes an example output to illustrate the aggregation format. However, it does not explicitly state whether the operation is read-only or has side effects, but the context strongly implies it is a non-mutating analysis (no annotations are provided).

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 well-structured with clear sections: opening statement, when to use, improvements list, example output, parameters, and returns. Every sentence contributes meaningful information, and the formatting makes it easy to scan. It is appropriately sized for the tool's complexity.

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?

The description is unusually complete: it gives usage context, differentiates from sibling tools, lists capabilities, shows a sample output, and summarizes the return value. Given there is no output schema and no annotations, the description effectively conveys what the tool does and what to expect. It also covers the key parameters that influence behavior.

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 baseline is 3. The description adds value by contextualizing the three boolean flags (include_suggestions, detect_quantifiers, detect_incomplete) in relation to the overall aggregation features, such as linking include_suggestions to 'suggests specific fixes with reasoning.' It does not repeat all schema fields, focusing only on those that drive the tool's unique behavior.

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 performs 'Smart grammar validation with aggregated, actionable insights' and immediately distinguishes it from validate-grammar by focusing on pattern detection rather than individual warnings. It enumerates specific capabilities (grouping, prioritization, suggestions, anti-pattern detection) that go beyond basic validation.

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?

Provides explicit when-to-use guidance: 'When validate-grammar returns too many issues and you need to see patterns, not individual warnings.' This names the alternative tool and gives clear context. The 'Improvements over basic validation' section further clarifies the tool's role versus simpler validation.

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