adapt_threshold

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

The description mentions 'reinforcement learning' but does not disclose side effects, required data, or state changes. Without annotations, the description carries the full burden, yet it provides minimal behavioral insight.

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 very short (one sentence) but fails to convey necessary detail. It is concise but under-specified, so it does not earn high marks for efficiency.

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 tool's complexity (RL algorithm, four parameters, no output schema), the description is highly incomplete. It lacks parameter explanations, output description, and usage context, making it inadequate for an agent to use effectively.

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%, and the description does not explain any parameters. 'fpRate', 'blockRate', 'callVolume' are not described; only 'parameter' is hinted at via the resource list. This is severely lacking.

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 adaptively tunes rate limit, latency limit, or confidence threshold using reinforcement learning. The verb 'tune' and specific resources are mentioned, distinguishing it from sibling tools like 'tune_policy_rule' which may use different methods.

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?

No guidance on when to use this tool versus alternatives. No context on prerequisites, limitations, or appropriate scenarios (e.g., when RL is needed vs manual tuning).

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