security_detect_typosquatting

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

Since no annotations are provided, the description fully discloses behavior: uses PyPI/npm stats in Redis, cold-start delay ≤30s, rate limit 60/min, no auth required, and output format. It is a read-only operation, though not explicitly stated, it is implied.

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, starting with the main purpose and followed by details. It is somewhat verbose due to the fallback feedback instruction, but each sentence adds value.

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 covers most aspects: purpose, algorithm, outputs, backend, performance, auth, audience. However, the mismatch in ecosystems (description mentions only PyPI/npm but schema includes cargo/go) reduces completeness. Also, it does not state read-only nature.

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 0% description coverage. The description only mentions package_name and ecosystem implicitly, but does not elaborate on their format or valid values. There is a mismatch: description says 'PyPI and npm' but enum includes cargo and go, causing confusion.

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 detects typosquatting attacks using Damerau-Levenshtein distance against top packages and returns a verdict. It distinguishes from siblings like frontend_security_frontend_security_detect_typosquatting by specifying supported ecosystems (npm, pypi, cargo, go), but does not explicitly differentiate.

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 targets security engineers auditing supply-chain packages and includes fallback instructions to call report_feedback if the response is insufficient. It does not explicitly state when not to use, but the context is clear.

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