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ZeroWidth Caliper

Create a Caliper rubric

caliper_rubrics_create

Creates the scoring rubric an eval's LLM judge uses — 1-10 criteria, each scored on a numeric scale (default 1-5), judged pass/fail (kind pass_fail), or checked in code with no judge (kind check: contains, not_contains, matches a regex, valid_json, max_chars, equals_expected). Write criteria about the FLOW'S JOB (accuracy to source material, tone, refusal behavior), not generic 'quality'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesRubric name (2-120 chars).
criteriaYes
workspaceNoWorkspace slug override.
approvalIdNoApproval id from a prior needs_confirmation response. Omit on the first call.
visibilityNoWho can see it: PRIVATE (only the user), WORKSPACE (every member, the default), or SHARED (specific people, granted afterwards). Say 'make it private' → PRIVATE.
descriptionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the create-and-safe profile is covered. The description adds the default scale (1-5) and the semantics of each criteria kind, but does not mention the approval/confirmation flow implied by approvalId or any workspace scoping behavior.

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

Conciseness3/5

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

Front-loaded with purpose and the guiding heuristic is well placed, but the middle sentence is a dash-chained dump of every check type that reproduces the schema enum verbatim. It is dense without being fully wasteful, but those enumerated values do not earn their place in the description.

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?

No output schema exists, and the description plus the fairly rich input schema together cover the required title/criteria and their structure well. Remaining gaps (approvalId, visibility, workspace) are already documented in the schema, so the description is nearly complete for this create operation.

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

Parameters3/5

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

Schema coverage is 67%, so the schema documents most fields. The description restates the kind values (scale, pass_fail, check) and enumerates the check types, largely duplicating schema enum descriptions rather than adding syntax or constraints. Baseline 3 is appropriate for mid-level coverage where the description re-explains rather than extends.

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?

States a specific verb and resource (creates the scoring rubric) and pins down its domain role ('an eval's LLM judge uses'), plus the concrete shape (1-10 criteria, three kinds). It is clearly distinguishable from rubrics_get/list/update/delete siblings.

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?

Offers real guidance on how to author criteria ('about the FLOW'S JOB ... not generic quality'), which is valuable. However, it never says when to reach for this tool versus caliper_rubrics_update or how it relates to evals_create — usage is only implied.

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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