frontend_security_detect_typosquatting

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

Describes algorithm (Damerau-Levenshtein distance ≤ 2), return fields, and safety (read-only, no side effects, idempotent). No annotations provided, so description carries full burden and does so thoroughly.

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?

Well-structured with clear sections: purpose, usage, parameters, algorithm, safety, fallback. Every sentence adds value without redundancy.

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?

Covers all aspects: purpose, usage, parameters, algorithm, safety, and fallback. Output schema exists, so return values are not needed in description. Complete for an AI agent.

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 has 0% description coverage, so description must compensate. It explains both parameters: package_name is required, ecosystem defaults to npm with enum values. Provides default value and required status, but could add format or constraints.

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?

Clearly states it detects typosquatting for frontend packages using a curated corpus of top 500 frontend packages. Distinguishes from sibling security_detect_typosquatting for backend packages.

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?

Explicitly tells when not to use (for backend packages) and provides alternative sibling tool. Also directs to report_feedback if tool response is insufficient.

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