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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains that audit results are based exclusively on the provided package list, that unsupported ecosystems will be ignored, and that outputs include structured JSON and a Markdown summary. It does not mention side effects or read-only guarantees, but for an audit tool the behavior is well disclosed.

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 long but efficiently organized with clear headings: what it does, when to use, supported ecosystems, inputs, outputs, and LLM guidelines. Each section earns its place and the opening one-sentence summary is front-loaded, making the tool's purpose immediately understandable.

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?

Given the tool's moderate complexity and the presence of an output schema, the description covers all essential operational context: supported ecosystems, input sources, output types, and usage guardrails. An agent has enough information to decide when to call it and how to construct valid PURLs in the correct format.

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?

Although the input schema already documents the 'purls' array with 100% coverage, the description adds substantial meaning by explaining how to generate PURLs from manifest files for each ecosystem and providing concrete examples per ecosystem. This goes well beyond the schema's simple 'List of Package URLs (PURL) to audit' and prepares the agent to construct valid input.

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 opens with a specific verb and resource: 'Perform a software package vulnerability audit using SecDB.' It clearly distinguishes itself from sibling tools like vulnerability_search or vulnerability_info by focusing on auditing a list of PURLs and returning a report plus summary, so an agent can tell them apart without opening schemas.

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 'When to use this tool' section gives explicit conditions such as determining whether dependencies contain known vulnerabilities or which packages require patching. It also provides LLM usage guidelines like 'Never guess whether a package is vulnerable — always call this tool' and restricting input to supported ecosystems. It does not explicitly name sibling tools or state when to prefer them, 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.