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

Beyond the read-only/idempotent annotations, it discloses the Damerau-Levenshtein threshold, the top-10,000 comparison set, the returned verdict format, Redis-backed data, a cold-start penalty of ≤30s, the 60/minute rate limit, and that no auth is required. This is rich behavioral disclosure.

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?

Purpose and behavior are front-loaded in compact sentences. The addition of the rate limit, cold-start, auth, and feedback instructions make it longer than strictly necessary, but most sentences carry operational 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 purpose, algorithm, outputs, performance, auth, and rate limiting, and an output schema exists. It is slightly incomplete for the cargo and go ecosystem values because it only mentions PyPI and npm download stats, leaving their behavior implicit.

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?

Both parameters are fully described in the input schema (100% coverage), including an enum for ecosystem, so the description does not need to repeat parameter details. The description adds algorithm context but no extra per-parameter semantics.

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 names a specific verb and resource: 'Detect typosquatting attacks against a package name', and adds the matching algorithm and output verdict. It is clear, though it does not explicitly differentiate itself from the closely named sibling frontend_security_detect_typosquatting.

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?

It gives a concrete use context: 'For security engineers auditing supply-chain package names before inclusion'. This communicates when the tool is appropriate, though it does not name alternatives or state when not to use it, so it stops short of full routing guidance.

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