adaptive_get_recommendation

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the return type, contents (layout changes, confidence, reasoning), and the null return when no recommendation exists. It does not disclose potential side effects, but the 'get' nature implies a read-only operation, and the description adds meaningful behavioral context beyond the tool name.

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 three sentences, each earning its place: the first states the primary purpose, the second lists the response contents, and the third covers the null case. It is front-loaded with the key action and avoids any 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 getter with no parameters and no output schema, the description fully explains the return value, including what is included (layout changes, confidence, reasoning) and the null condition. No additional context is needed for effective use, making it complete given the tool's simplicity.

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 the schema requires no additional description. The description inherently provides all needed parameter context (i.e., none). Per the baseline for 0 parameters, a score of 4 is appropriate since no parameter explanations are necessary.

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 uses a specific verb ('returns') and identifies a clear resource ('most recent layout recommendation from the adaptive UI system'). It also distinguishes the tool from siblings by specifying its content (layout changes, confidence, reasoning), which sets it apart from tools like adaptive_get_accuracy or adaptive_reject_recommendation.

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 for retrieving the latest recommendation, but it doesn't explicitly state when to use it compared to alternatives like adaptive_accept_recommendation or adaptive_reject_recommendation. There is no mention of prerequisites, exclusions, or a 'use this when' context, so guidance is only 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.