Extend MCP

Update an evaluation item

update_evaluation_item

DestructiveIdempotent

Replace one evaluation item's expected output (evaluations group). The file pairing cannot change — delete and re-add for that. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`itemId`	Yes	Item ID (evi_...).
`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.
`expectedOutput`	Yes	Ground truth matching the evaluated resource type. Extractor sets: { value: { <fields matching the extractor's schema> } }. Classifier sets: { id, type, confidence? } (the winning classification). Splitter sets: { splits: [{ identifier?, classificationId?, startPage, endPage }] }.
`evaluationSetId`	Yes	Evaluation set ID (ev_...).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`file`	Yes
`expectedOutput`	No
`evaluationSetId`	No

TDQS

A4.4/5.0

Behavior4/5

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

With destructiveHint and idempotentHint already present in annotations, the description adds useful behavioral context: only the expected output is replaced, the file pairing is immutable through this operation, and deletion/re-adding is the route for pairing changes. This goes beyond the annotations without contradicting them.

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 compact, front-loaded with the core behavior, and every sentence earns its place. The key constraint and the alternative path are conveyed in two sentences with no redundant filler.

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 input schema, output schema, and annotations, the description covers the essential behavioral constraint and usage nuance. It could go slightly further by naming the specific alternative sibling tool or clarifying how llmContext guidance appears, but it is essentially complete for an agent selecting and invoking the tool.

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 does not add parameter-level detail beyond the schema, but it does clarify the conceptual scope of the update ('expected output') and the immutable file pairing.

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 ('Replace') on a precise resource ('one evaluation item's expected output') and clarifies that this belongs to the 'evaluations group.' It also distinguishes itself from sibling tools by noting that file pairing cannot be changed here and requires delete/re-add.

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?

The description explicitly provides a when-not-to-use condition: if the file pairing needs to change, do not use this tool; instead delete and re-add the item. It also instructs the agent to follow any llmContext guidance included in results, giving clear contextual direction.

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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