auto_map_controls

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'Mutating: writes control-to-requirement mappings,' discloses the background job behavior (20-45s), and says the tool waits for completion and returns the result. It also clarifies that it never adds or excludes entities, which is a key safety behavior.

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 a compact two-paragraph structure that front-loads the core purpose, then covers behavior and comparison. Every sentence adds value: the first sentence states what it does, the second covers requirements and mutating nature, the third covers background execution, and the final sentence explicitly contrasts with sibling tools. No fluff or repetition.

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 complete for a tool with an output schema. It covers the purpose, mutation risk, timing, PRO tier, and relationship to alternatives. The output schema exists, so no need to describe return values. The contrast with siblings is particularly helpful for an AI agent deciding among map_control_to_requirement, auto_map_controls, and auto_remediate.

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 description coverage is 75%, with model_id, control_id, and framework_id already documented in the schema. The description adds context about the overall mapping operation but does not provide additional parameter-level meaning, leaving server_version without any description. Since the description does not compensate for this gap and the schema already covers most parameters, the score is baseline-appropriate.

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's purpose: 'LLM-map a model's existing controls to a framework's requirements.' It uses a specific verb and resource, and explicitly differentiates from sibling tools by positioning itself between map_control_to_requirement and auto_remediate. The scope is precise: it only creates mappings from existing controls and never adds or excludes entities.

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 explicit guidance on when to use this tool versus alternatives. It names map_control_to_requirement as the manual one-mapping-at-a-time option and auto_remediate as the full loop that also excludes non-applicable requirements and proposes new entities. It also mentions the PRO tier requirement and that it waits for completion, clarifying usage context.

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