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get_dataset_status

Idempotent

Check a dataset's readiness for training by retrieving its status and successfully uploaded files. Only READY datasets can be mounted by training jobs.

Instructions

Get a dataset's status and its successfully uploaded files.

Backs ``GET /prod/v1/trainers/datasets/{dataset_id}/status``.
Lifecycle: ``DRAFT`` → ``UPLOADING`` → ``READY`` (or ``FAILED``,
which sets ``error``). Only ``READY`` datasets can be mounted by a
training job. Files still uploading or failed do not appear in
``files``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A3.6/5.0
Behavior1/5

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

The description says 'Get' and 'Backs GET /prod/v1/trainers/datasets/{dataset_id}/status', clearly presenting a read-only operation. The annotations declare readOnlyHint=false, which contradicts that characterization. Because the description conflicts with the annotation, behavioral transparency must be scored as 1.

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?

Four sentences, no filler. The main purpose is front-loaded, the endpoint mapping is compact, and the lifecycle is given as a concise chain. Every sentence contributes useful information.

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?

The description covers the lifecycle states, the error field, file filtering, and the practical consequence of mounting. With no output schema, it reasonably explains return behavior. It is only slightly incomplete in not detailing the shape of files or error objects, and the contradiction with readOnlyHint prevents full completeness.

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 only parameter, dataset_id, is present in the schema with type and title. The description embeds dataset_id in the endpoint path but adds no additional format, constraints, or usage semantics beyond what the schema already makes clear. Baseline 3 applies because schema coverage of this single parameter is effectively complete.

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 clear verb ('Get'), a specific resource ('a dataset's status and its successfully uploaded files'), and the lifecycle behavior. It is easily distinguished from sibling status tools because it is explicitly scoped to datasets and references the exact endpoint.

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 lifecycle explanation and the statement that 'Only READY datasets can be mounted by a training job' provide useful context for when to use this tool. However, it does not explicitly mention alternatives or state when not to use it versus other status tools like get_training_job_status.

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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