rai_benchmark_promptsReturns benchmark question sets for truthfulqa, bbq, and hellaswag so you can collect model responses before scoring them with the evaluation tool.
Return the question set for a benchmark suite. Use to collect model responses before calling rai_benchmark. Suites: truthfulqa, bbq, hellaswag.
| Name | Required | Description | Default |
|---|---|---|---|
| suite | No | truthfulqa |
Changes observed during successful MCP inspections.
v1.2.6Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds the pipeline position (call before rai_benchmark), which is genuinely useful workflow context, but says nothing about the size or shape of the returned set.
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 short sentences, nothing wasted, with the core action front-loaded and the alternative/sequencing information following immediately.
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-enum getter with no output schema, the description covers purpose, workflow placement, and valid suites. It does not describe the return payload (e.g., question format or count), which is the only remaining gap.
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?
Only one parameter exists and schema description coverage is 0%, but the parameter is an enum whose values are self-explanatory. The description restates the enum values ('truthfulqa, bbq, hellaswag') but does not explain the default or what each suite measures, so it adds marginal value.
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?
States a specific verb and resource ('Return the question set for a benchmark suite') and explicitly positions itself relative to the sibling rai_benchmark, so an agent can distinguish gathering prompts from running the suite. No ambiguity about what is returned.
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?
Explicitly says to use it to collect model responses before calling rai_benchmark, giving clear workflow context and sequencing. It lacks explicit exclusions (e.g., when not to use it), which keeps it short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.