restore_assumption

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

With no annotations, the description fully covers behavioral traits: it creates a new model version, leaves control group structure intact, and requires re-attestation. It lacks details on server_version or error conditions, but covers the main mutation effects.

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 4 sentences, front-loaded with the main operation, then providing necessary behavioral details. No redundant 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?

Given there is an output schema (not shown), the description is complete enough, explaining what the tool does and its side effects. The only minor gap is the purpose of server_version, but overall it's sufficient.

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 67%, and the description does not add additional meaning to the parameters beyond what the schema provides. Parameters model_id and assumption_id are described in schema; server_version is not, but the description does not clarify its role.

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 restores a soft-deleted assumption, includes the key effect (returns to active status), and explains side effects on controls and re-attestation, distinguishing it from sibling tools like remove_assumption or add_assumption.

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 implies when to use (when an assumption is soft-deleted) and mentions the re-attestation requirement, but does not explicitly state when not to use or compare to alternatives. The usage is clear from context.

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