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

Goes well beyond the annotations: detail/subrequest caps, explicit reporting of omitted details and incomplete coverage, 'missing evidence is unknown, not zero risk', 'automated signals, not a safety guarantee', and pricing/failed-call behavior. It even pre-empts the readOnlyHint=false question by explaining the call is charged, so nothing looks contradictory.

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?

Front-loads the core capability, then layers caveats, example and pricing in a logical order with no filler. It is dense with multiple clauses per sentence, which slightly hurts scannability, but every sentence carries real 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?

With no output schema, the description still conveys the shape of results (advisory IDs, OSV affected/fixed ranges, dated EPSS estimates) plus limits and cost. An agent has everything needed to decide and call correctly.

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 coverage is 100%, so the baseline is 3, but the description adds a concrete worked example (npm:lodash@4.17.20,pypi:requests@2.31.0,npm:@types/node@22.0.0) that reinforces the format and scoped-name syntax in a usable form.

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?

States a specific verb and resource: audits up to 10 public dependencies for advisory IDs, OSV affected/fixed ranges and EPSS estimates. It clearly distinguishes itself from generic siblings like 'package', 'pypi' and 'npm_downloads' by naming the exact artifacts it returns.

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?

Gives a clear trigger ('Use before installing or upgrading dependencies') and constraints (max 10, scoped npm syntax, exact-version pinning). It does not name an alternative sibling tool or a when-not condition, so it stops short of a 5.

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