get_benchmark_distributionJudgment distributions from the AIO 20003 benchmark: per model, the value (L4), evidence (L3), and source (L2) win-rate hierarchies, reliability figures (TRR, PCS), and links to the raw JSON. Omit "model" to get every measured model. CC BY 4.0.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Model slug, e.g. "gpt-5-nano". Omit to list all measured models. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context beyond annotations by detailing the data contents (value L4, evidence L3, source L2 hierarchies, reliability figures) and the license (CC BY 4.0). This provides meaningful behavioral context without contradicting annotations.
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 concise and front-loaded, using two sentences to convey the tool's output, parameter usage, and licensing. Every sentence adds value, with no redundancy or unnecessary fluff.
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 has one optional parameter and no output schema, the description adequately communicates what data the user will receive (distributions, reliability figures, links) and how to control the model parameter. It could be more explicit about the return format, but it is sufficient for effective use.
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%, so the parameter 'model' is fully documented. The description repeats the same instruction ('Omit "model" to get every measured model') as the schema, adding no new semantics. Baseline 3 is appropriate since the schema carries the complete parameter information.
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 judgment distributions from the AIO 20003 benchmark, with specific components (value/evidence/source win-rate hierarchies, reliability figures, links to raw JSON). It uses a specific verb and resource, and it distinguishes itself from sibling tools like get_bench_items by focusing on distributions rather than raw items.
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 provides clear context: it explains what the tool returns (per-model distributions) and how to control the model parameter ('Omit "model" to get every measured model'). However, it does not explicitly compare with alternatives or state when not to use this tool, so it lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct resource or action: fetching items vs. distributions, listing vs. fetching specific entities, and distinct submission endpoints for benchmark, eval, and RFC comments. Even the two 'get items' tools (bench vs. eval) are clearly differentiated by their descriptions and use cases.
All tool names follow a consistent verb_noun pattern with lowercase and underscores. Verbs are grouped by action type (get_, list_, submit_, start_, register_, verify_, search_), and the patterns are predictable and uniform across the set.
At 17 tools, the set is slightly above the ideal 3-15 range but each tool serves a distinct purpose. The breadth of the domain (papers, RFCs, standards packs, benchmarks, evaluation, certification, vocabulary, search) justifies the count, and no tool feels redundant.
The tool surface covers the major workflows: listing and fetching resources, starting and submitting evaluation attempts, registering and verifying certifications, and submitting benchmark/RFC responses. Minor gaps exist, such as no direct tool to cancel an evaluation attempt or list a specific benchmark run by id, but these are edge cases and the core lifecycle is well-covered.