query_hyperparameter_priors

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

No annotations are provided, so the description carries the full disclosure burden. It does describe the return shape ('suggestion table with ranges and KB-grounded justification'), which is valuable given there is no output schema. However, it says nothing about grounding limits, freshness, permissions, or whether results are cached — behavioral traits beyond a bare lookup are undisclosed.

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?

Front-loaded with the core purpose, then a scannable 'Use this tool when' list and a 'Returns:' line. Slightly redundant between the opening sentence and the 'Look up recommended parameter values...' sentence, but every part is short and earned.

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-parameter read tool with no output schema and no annotations, the description covers purpose, usage triggers, and return format, which is close to sufficient. The main residual gap is not distinguishing itself from 'search_knowledge', leaving an ambiguous choice between two documentation-lookup siblings.

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 100% and the schema already supplies free-form query examples, so 3 is the baseline. The description adds meaning above baseline by enumerating what dimensions a good query should encode (hardware, model size, task type, scale), which helps the agent formulate inputs the tool can actually serve.

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?

Specific verb ('Query'/'Look up') plus a concrete resource (documented configuration values, recommended ranges, tuning heuristics for frameworks and libraries). An agent can tell it is a knowledge lookup, not an action tool. It does not, however, differentiate itself from the sibling 'search_knowledge', which appears to overlap in scope.

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 explicit 'Use this tool when you need:' block gives three clear triggering conditions (defaults, ranges/tuning strategies, context-specific suggestions), which is strong positive guidance. It lacks any when-not condition or named alternative, so it falls short of a 5 that would route the agent away from 'search_knowledge' when appropriate.

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