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Add evaluation items (bulk)

add_evaluation_items

Bulk-add ground-truth examples (1-100 per call) to an evaluation set (evaluations group). Each item pairs an already-uploaded file with the output the resource SHOULD produce for it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes1-100 ground-truth items.
environmentYes"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
workspaceIdYesTarget workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.
evaluationSetIdYesEvaluation set ID (ev_...).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
itemsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already mark this as non-read-only, non-idempotent, and non-destructive; the description adds useful context by stating the batch size limit and the prerequisite that files must already be uploaded. However, it does not clarify behavior on duplicate items, whether existing items are appended or replaced, or what side effects may occur. This leaves some behavioral ambiguity for a mutating operation.

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?

The description is two sentences with no redundant filler. The key information—bulk add, quantity range, target resource, item composition—is front-loaded and every sentence contributes meaning.

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?

Given the rich parameter schema, the prerequisite about already-uploaded files, and the presence of an output schema, the description is largely complete for an agent deciding whether and how to invoke the tool. A small gap remains around duplicate handling and append-vs-replace semantics, but this does not block correct invocation in the common bulk-add use case.

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 description coverage is 100%, so the schema already documents all four parameters in detail. The description adds no parameter-specific meaning beyond what the schema provides, which is acceptable but not additional value.

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 states a specific action (bulk-add ground-truth examples), a clear resource (evaluation set), and a defined scope (1-100 items per call). It also clarifies what each item consists of: an already-uploaded file paired with expected output. This clearly distinguishes it from related tools like update_evaluation_item or create_evaluation_set.

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

Usage Guidelines4/5

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

The description gives clear context for when to use this tool: when adding multiple ground-truth examples in bulk to an evaluation set, with an explicit batch limit. It does not name alternatives or exclusions, but the bulk-addition framing and prerequisite that files must already be uploaded provide sufficient guidance for typical use.

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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