Extend MCP

Delete an evaluation item

delete_evaluation_item

DestructiveIdempotent

Permanently remove one ground-truth item from an evaluation set (evaluations group). Past run metrics are unaffected. This cannot be undone. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`itemId`	Yes	Item ID (evi_...) to delete.
`environment`	Yes	"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
`workspaceId`	Yes	Target workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.
`evaluationSetId`	Yes	Evaluation set ID (ev_...).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`deleted`	Yes

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already mark destructiveHint and readOnlyHint false, and the description adds important non-obvious behavior: deletion is permanent, cannot be undone, and past run metrics are unaffected. This goes beyond the structured hints and clarifies the side-effect profile for a destructive operation.

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?

The description is short and front-loaded: the first sentence states the core purpose and the second states the key side effect. Minor redundancy exists between 'Permanently remove' and 'This cannot be undone,' and the final instruction about llmContext guidance is vague and less directly useful for tool invocation.

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?

With an output schema present and annotations covering the destructive safety profile, the description adds the essential non-obvious facts: permanence, irreversibility, and metric preservation. The instruction to follow llmContext guidance hints at handling result-provided context, though it remains somewhat unclear.

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 baseline is 3. The description repeats domain concepts like 'ground-truth item' and 'evaluation set' but does not add ID formats, environment constraints, or value semantics beyond what the schema already documents.

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 first sentence names a specific action ('Permanently remove'), a specific resource ('one ground-truth item'), and the containing context ('evaluation set (evaluations group)'). This clearly distinguishes the tool from sibling delete_* tools and from update_evaluation_item by focusing on item-level removal.

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 does not explicitly name alternatives such as update_evaluation_item or add_evaluation_items, but it supplies clear decision-relevant context: the operation is permanent and past run metrics remain unaffected. This lets an agent infer that the tool is for removing items without altering historical evaluation results.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct resource+action combination, and the descriptions actively disambiguate potential overlaps (e.g., extract_data vs parse_document, detect_form_fields vs edit_pdf, get_file vs get_file_upload). The consistent verb_noun prefix pattern makes the semantic boundary of every tool immediately recognizable.

Naming Consistency4/5

The dominant verb_noun pattern is highly consistent across all nine domains (list_*, get_*, create_*, update_*, delete_*, run_*, get_*_run, get_*_batch, publish_*_version). Minor deviations exist: deploy_workflow_version vs publish_*_version for the same freeze-a-draft concept, and get_form_detection_run doesn't mirror its detect_form_fields counterpart.

Tool Count2/5

86 tools is a very heavy agent-facing surface, well past the 25+ threshold. The count is inflated by the near-identical 13-tool lifecycle repeated across extract, classify, and split (each with list/get/create/update/publish/runs/batches/versions), and while each tool has a distinct purpose, the sheer volume makes selection harder.

Completeness4/5

Core lifecycles are thoroughly covered: create → update → publish → run (single and batch) → poll → cancel → delete-run → list runs/versions. Notable gaps include no delete tool for extractors, classifiers, splitters, workflows, or evaluation sets, and edit/form-detection runs have no list endpoint (documented workaround: keep run IDs). These are hygenic gaps that don't block primary workflows.

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