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ZeroWidth Caliper

by ai.zerowidth
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Generate more items for a Caliper dataset (spends credit)

caliper_datasets_generate_items

Appends model-written items to an EXISTING dataset, in the style of what's already there (existing items are the few-shot examples; the output shape matches theirs unless overridden). Use to widen coverage when the user says 'more like these' or 'add edge cases'; write them by hand with caliper_datasets_add_items when the scenarios are known. SPENDS workspace inference credit, so it sits behind the approval gate — say so. Existing items and their ratings are untouched.

Input Schema

NameRequiredDescriptionDefault
countNoItems to add, 1-20. Default 5.
shapeNoWhat each generated item is: `qa` (one input, the default), `sequence` (2-6 scripted user turns the model answers one at a time, with expectedResponse + expectedBehavior), or `simulated` (a person Caliper plays adaptively: goal, persona, disposition, expectedBehavior). outputsMode applies to `qa` only.
datasetIdYesDataset to extend, from caliper_datasets_list.
workspaceNoWorkspace slug. Personal tokens with no default workspace MUST pass this; tokens with a default can override per call. Ignored for workspace API keys.
approvalIdNoApproval id from a prior needs_confirmation response. Omit on the first call.
descriptionNoWhat to bias toward, e.g. 'angry customers', 'ambiguous refund questions'.
dispositionNoWith shape `simulated`: how every generated person behaves — a preset id (genuine, pressure, confused, impatient, vague, non-native) or free text. Omit to let the generator vary it from person to person.
outputsModeNoWhat each generated item carries beyond the input: `expected` (golden answers — eval-ready, the default), `none` (inputs only — for a spec others fill in), `captured` (sample answers to rate in a review).
anchorItemIdNoAn item id (from caliper_datasets_get) the new items should resemble most.
expectedStyleNoWith outputsMode `expected`: `verbatim` literal reference answers (default) or `conditions` — what a correct answer must do, when there's no single right wording.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), it discloses the cost model ('SPENDS workspace inference credit'), the approval gate and the requirement to tell the user, and the safety guarantee that existing items and ratings are untouched. These are material behaviors an agent cannot infer from structured fields.

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

Conciseness4/5

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

Three dense sentences, front-loaded with the append-to-existing-dataset behavior, then routing, then cost. Every clause carries information, though the credit/approval sentence is slightly packed. No filler or repetition.

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?

For a ten-parameter mutation tool with no output schema, the description covers behavior, sibling routing, cost, and non-destructiveness. It does not explain what a successful response returns or how approvalId round-trips, but the schema covers the confirmation flow and no output schema exists to explain.

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 100%, so the schema already documents all ten parameters and their enums; the baseline is 3. The description adds only the inheritance rule ('output shape matches theirs unless overridden') and the few-shot style hint, which lightly supplements rather than meaningfully extends the schema.

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 ('appends model-written items to an EXISTING dataset') plus the mechanism (existing items are the few-shot examples). It explicitly distinguishes itself from the sibling caliper_datasets_add_items, so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Gives explicit trigger conditions ('more like these', 'add edge cases') and names the alternative with the condition that selects it ('write them by hand with caliper_datasets_add_items when the scenarios are known'). When-to-use and when-not are both covered.

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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