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list_eval_rubrics

Retrieve organization-defined LLM evaluation rubrics with criteria, scoring grids, and judge providers. Identify auto-scanner rubrics triggered after every job.

Instructions

List LLM-as-judge evaluation rubrics defined for the organisation.

A rubric defines evaluation criteria (accuracy, safety, relevance…), a scoring grid (0–10 with justification), and the LLM provider used as judge (Claude, OpenAI, Ollama, Groq). Rubrics marked is_auto_scanner=true trigger automatically after every completed job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided; description reveals auto-scanner behavior but does not explicitly state read-only nature or any security/permission requirements. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose, second adds details. No wasted words, front-loaded, and very concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Good coverage for a simple list tool with no output schema and no annotations. Missing details on potential implicit filters or ordering, but adequate overall.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in schema; description adds meaning by explaining what each rubric contains, providing context beyond the empty schema. Baseline 4 for 0 params.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List LLM-as-judge evaluation rubrics' with a specific verb and resource, and distinguishes from siblings by detailing what a rubric includes (criteria, scoring grid, LLM provider, auto-scanner flag).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage when needing to view available rubrics, but lacks explicit guidance on when not to use or alternatives like evaluate_job or get_job_evaluations.

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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