Extend MCP

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. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`items`	Yes	1-100 ground-truth items.
`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
`count`	Yes
`items`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already convey write behavior and non-idempotency. The description adds meaningful behavioral context beyond annotations: it requires already-uploaded files, enforces a batch size limit, clarifies that items are ground-truth pairings, and instructs the agent to follow llmContext guidance in results. This is useful, non-redundant behavioral detail.

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 fluff. It front-loads the core action and batch constraint, then explains the item structure, and closes with an actionable instruction about llmContext. Every sentence earns its place.

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 detailed schema, output schema, and annotations, the description provides sufficient context for correct invocation. It covers batch limits, item composition, and response-related guidance. It could theoretically warn about environment pinning or duplicate handling, but those are already addressed or inferable from the schema.

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?

The input schema already provides 100% coverage with detailed descriptions for every parameter, including nested expectedOutput formats. The description adds only general framing ('ground-truth examples', 'already-uploaded file') without introducing new parameter-level meaning, so a baseline score of 3 is appropriate.

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 clearly states the tool's function: bulk-add ground-truth examples to an evaluation set. It specifies the resource ('evaluation set'), the action ('bulk-add'), and the content of each item (file + expected output), making it easy to distinguish from sibling tools like run_evaluation or list_evaluation_items.

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 establishes clear usage context: adding ground-truth examples in batches of 1-100, pairing uploaded files with expected outputs. It does not explicitly name alternatives or exclusions, but the purpose is clear enough that an agent can infer when to use it versus update_evaluation_item or delete_evaluation_item.

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.

Resources

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