apply_mutation

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

No annotations are provided, so the description carries the full burden. It discloses the critical authorization requirement (human key) and the fact that the LLM cannot self-approve, which is valuable. However, it does not mention what happens on invalid key, whether the operation is destructive or reversible, or any side effects. For a mutation tool, this is a moderate disclosure but not exhaustive.

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 two sentences with no filler. The purpose and the critical condition are front-loaded, and the human-in-the-loop constraint is stated directly. Every sentence earns its place, making it highly concise and well-structured.

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?

An output schema exists, so return values need not be explained. The description covers the essential usage constraints (approval key, previous vetting). However, it does not explicitly mention the need to obtain a proposal token via propose_mutation, which is a logical prerequisite. Given the sibling list, this is inferable, so the completeness is high but not perfect.

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 0%, so the description must compensate. It gives meaning to proposal_token as a 'previously vetted mutation proposal' and operator_approval_key as a 'secret key' provided by a human administrator. This goes beyond the bare property names, but it does not explain where to obtain the proposal_token (e.g., from propose_mutation) or the format of the key. The semantics are partially explained but not fully.

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: it executes a previously vetted mutation proposal, with a strict condition (valid operator approval key). This is a specific verb+resource+condition, and it distinguishes itself from the sibling propose_mutation by focusing on execution rather than proposal creation.

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 the workflow: use this tool when you have a proposal_token and an operator_approval_key. It states that the LLM cannot self-approve, indicating that human approval is required, but it does not explicitly name propose_mutation as the source of the token or contrast with alternatives like reset_quarantine. The context is clear but not fully explicit about the prerequisite step.

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