easydeploy-ai-mcp

Official

by EasyDeploy-AI

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

List dataset versions

list_dataset_versions

Read-onlyIdempotent

List all versions of a dataset to view version numbers, types, QA status, and row counts. Use it to identify a trainable dataset version before submitting a training job.

Instructions

List all versions of a dataset (version number, version_type, qa_status, row counts).

project_id is optional — the backend resolves access from dataset_id when omitted. Column names, where present, come back as a columnNames list.

qa_status is informational and does not gate anything. version_type matters in one case: submit_training_job with an explicit dataset_version_id rejects a raw version. Omit dataset_version_id to train on the model version's own dataset.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dataset_id`	Yes
`project_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 29, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive, so the bar is lower. The description still adds real behavioral context: project_id is optional and the backend resolves access from dataset_id, and qa_status is informational rather than gating.

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?

Front-loaded with the purpose and returned fields, then scoped notes. It loses a little economy to the trailing submit_training_job/version_type digression, which belongs more naturally in that sibling's documentation.

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?

An output schema exists, so return values needn't be explained, and the description still covers the optionality and resolution behavior of the one tricky parameter plus qa_status semantics. It is complete enough for an agent to call correctly.

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 coverage is 0%, so the description must carry the burden. It adequately explains the one non-obvious parameter by stating project_id is optional with backend resolution via dataset_id; the required dataset_id is self-evident.

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?

States a specific verb+resource ("List all versions of a dataset") and even enumerates the returned fields. It is clearly distinguishable from the singular sibling get_dataset_version and from list_datasets, so an agent can select it without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by "all versions" versus the singular get_dataset_version, and the qa_status/version_type commentary gives useful context. But the version_type guidance actually concerns submit_training_job, not when to call this tool, and no explicit alternatives or exclusions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.