adaptive_get_accuracy

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the output (a percentage) and the underlying logic (acceptance vs rejection), but it does not detail how the percentage is computed, whether it is session-based, or any other behavioral nuances. It is transparent at a basic level 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 short sentences, front-loaded with the primary purpose and immediately followed by a clarifying statement. Every word earns its place, with no redundancy or filler.

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 simple no-parameter getter with no output schema, the description sufficiently explains what is returned and the metric's meaning. It does not specify when data is available or how to interpret extreme values, but those are minor gaps given the tool's simplicity. The presence of related sibling tools provides surrounding context.

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?

The tool has zero parameters, so there is nothing for the description to clarify beyond the schema. According to the rule, a baseline of 4 is appropriate when no parameters exist. The description adds meaning by explaining what the output represents, but parameter semantics are not applicable.

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 'Returns the feedback loop acceptance accuracy as a percentage.' This is a specific verb (returns) and resource (acceptance accuracy), and it distinctively differs from sibling tools like adaptive_accept_recommendation or adaptive_get_recommendation by focusing on the aggregate metric rather than individual actions or recommendations.

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 tool is used to check how often recommendations are accepted vs rejected, but it does not explicitly state when to use it or contrast it with alternatives. No exclusions or specific contexts are provided, making usage guidance merely implied rather than explicit.

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