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Remove one item from a Caliper dataset

caliper_datasets_remove_item
Destructive

Drops one item. Ratings and run results keyed to it are orphaned (kept in history, gone from the dataset). A dataset must keep at least one item. May return needs_confirmation.

Input Schema

NameRequiredDescriptionDefault
itemIdYesItem id, from caliper_datasets_get.
datasetIdYesDataset id.
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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Goes well beyond destructiveHint=true by explaining the exact blast radius: ratings and run results keyed to the item are orphaned, kept in history but removed from the dataset. It also discloses the minimum-item constraint and the possibility of a needs_confirmation response, which the annotations do not convey.

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?

Three short sentences, front-loaded with the core action, then consequences, then the constraint and confirmation behavior. No filler; every sentence carries signal.

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 destructive mutation with no output schema, the description covers the key risks (orphaned ratings/run results), the hard constraint (minimum one item), and the confirmation pathway. It could note permission or reversibility details, but the orphan/history distinction already answers the most important agent question.

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 all four parameters (including workspace token rules and approvalId) are already documented in the schema. The description's mention of 'May return needs_confirmation' lightly connects to the approvalId flow but adds no syntax or format detail beyond the schema.

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

Purpose4/5

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

States a specific verb+resource ('Drops one item') and scopes it to a single item, which sets it apart from bulk siblings like caliper_datasets_add_items or whole-dataset operations like caliper_datasets_delete. It never names a sibling explicitly, but the singular scope is clear enough for an agent to disambiguate.

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?

The precondition 'A dataset must keep at least one item' is a genuine usage constraint, but the description never states when to prefer this over caliper_datasets_update_item or how it interacts with the removal of multiple items. Usage is implied rather than guided.

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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