nanomem_volatility

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

With no annotations, the description carries the transparency burden. It discloses that results are measured from the memory's own revision log and explicitly warns 'These are measured statistics, not a prediction,' preventing over-interpretation. It does not cover edge cases or exact response shape, but the core behavior is honestly characterized.

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?

Three sentences, each earning its place: definition of what is measured, practical use case, and a caution about interpretation. There is no filler or redundant restatement of the tool name.

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?

For a single-optional-parameter tool with no output schema, the description covers the output content and decision context well. It could more explicitly describe how min_revisions affects the returned statistics, but the schema already covers that parameter.

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 coverage is 100%, so the schema fully documents min_revisions. The description uses the same 'restated' concept but does not add meaning or usage detail about the parameter beyond what the schema provides.

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 identifies the tool as a volatility measurement over remembered facts and enumerates concrete outputs: restatement count, typical interval between changes, and time the current value has stood unconfirmed. It does not explicitly contrast with siblings like nanomem_stats or nanomem_changes, but its focus on change frequency is distinctive enough.

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?

It gives a direct use case: decide which facts are probably stale and worth re-confirming with the user. It does not mention when to prefer alternative tools such as nanomem_history or nanomem_changes, so some routing guidance is left implicit.

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