Extend MCP

Run an evaluation

run_evaluation

Run an evaluation set against a version of its resource and score the results against the ground truth (evaluations group). Async: returns immediately with a bpr_... run ID — poll it with get_evaluation_run (NOT a get-batch tool, even though the ID looks like a batch). Defaults to the set's resource at its latest published version; pass entity to pin { id, version: "1.2" | "latest" | "draft" }. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`entity`	No	Which resource + version to evaluate.
`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_...).
`evaluationSetItemIds`	No	Run only these items (evi_...) instead of the whole set.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`status`	Yes
`llmContext`	No
`evaluationSetId`	No

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already show this is not read-only and not destructive. The description adds useful behavioral context: the call is async, returns immediately with a bpr_... run ID, defaults to the latest published version, and may return llmContext guidance to follow. It doesn't fully describe side effects, but it covers the key runtime behavior beyond the annotations.

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 and front-loaded: purpose, async behavior, polling route, version pinning, and follow-up guidance are each covered in a short sentence with no filler. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 5 parameters, nested entity object, and existing output schema, the description covers the essential runtime behavior an agent needs: async semantics, ID format, polling path, default version behavior, and llmContext follow-up. Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 parameters well. The description adds extra value by explaining the version shorthand, the default entity/version behavior, and by clarifying that the bpr_ run ID is polled via get_evaluation_run rather than a batch tool.

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 ('Run an evaluation set against a version of its resource') and the purpose ('score the results against the ground truth (evaluations group)'). It also clarifies the async return behavior and run ID, making it easy to distinguish from related run/batch tools.

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?

It explicitly instructs to poll with get_evaluation_run and warns 'NOT a get-batch tool, even though the ID looks like a batch'. It also explains the default behavior (latest published version) and how to pin a specific version, giving an agent clear when-to-use guidance.

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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