convert_assumption_to_controls

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

With no annotations provided, the description fully discloses side effects: removal from assumption_groups entries, dropping of empty groups, and control status reversion to not_implemented. It also clarifies that underlying assumptions are not deleted. This is comprehensive behavioral transparency.

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 concise with two paragraphs: the first explains the core action, the second details side effects. No unnecessary words; each sentence adds 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?

Given the complexity of the operation and the presence of an output schema (not shown but mentioned), the description adequately covers inputs, side effects, and outcomes. It provides sufficient context for an agent to understand the tool's impact.

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 descriptions cover model_id and assumption_id (2 of 3, 67% coverage), but server_version lacks description. The tool description does not add extra meaning for parameters; it relies on schema. With moderate coverage, a baseline score of 3 is 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 purpose: converting a violated or retired assumption to controls, generating controls for covered COs and retiring the CO linkage. It distinguishes from sibling tools like get_assumption or remove_assumption by specifying the conversion action.

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 explicitly states 'Use when an assumption is no longer valid and the system owner needs to implement controls instead,' providing clear guidance on when to use. It does not explicitly state when not to use, 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.