get_remediation

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

The description goes well beyond the annotations by disclosing local DB storage path, no network calls, nothing is executed, and suggestions are advisory only. It also reveals the None-return behavior and how to generate a missing suggestion. This adds context that annotations alone do not provide.

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 relatively long but well-structured: it starts with a [READ] tag and one-sentence summary, then details the parameter, return shape, and special cases. Every sentence adds value, though it could be slightly tightened; the density is justified.

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 is fully complete for the tool's complexity. It explains the parameter source, return fields, the None case, and the advisory-only nature. Given the simple single-param input and the presence of an output schema, the description covers all necessary context.

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?

With schema description coverage at 0%, the description fully compensates by explicitly defining violation_id as the required 'id' field from list_violations. This adds meaning beyond the bare schema, which only provides the parameter name and type.

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 verb 'Fetch' and the specific resource: the persisted LLM-generated remediation suggestion for one violation. It distinguishes from sibling tools by specifying it operates on a violation's remediation data, not on baselines, rules, or drift events.

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 provides clear usage context: violation_id comes from list_violations, and if no suggestion exists, it tells the user to generate one via the vmware-harden CLI advisor. While it doesn't explicitly list alternative tools or when not to use it, the context is unambiguous and includes a conditional workflow.

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