easydeploy-ai-mcp

Official

by EasyDeploy-AI

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Create or update dataset version

create_dataset_version

Destructive

Register an S3 file as a raw, QA-cleaned, or training dataset version, or update an existing version's QA status. Tracks dataset upload and QA state.

Instructions

Create or update a dataset version.

Create (register an S3 file as a new version): Required: version_type (raw | qa_cleaned | training), file_url, qa_metadata (freeform JSON with QA results). file_url accepts either the file's s3:// URL (s3://{bucket}/users/{userId}/...) or its bare S3 key starting with users/{userId}/ — the s3Key field that complete_upload returns as datasetVersion.s3Key and get_dataset_version returns as s3Key. Any other form (an https URL, a key with another prefix) is refused before calling the API. The file must be under your own storage prefix users/{userId}/; the API rejects any other location with 400.

Update (change qa_status on an existing version): Required: version_id, qa_status (pending | in_progress | ready | blocked).

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
`file_url`	No
`qa_status`	No
`dataset_id`	Yes
`project_id`	Yes
`version_id`	No
`qa_metadata`	No
`version_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.3.0Oct 2, 2026

addedInput schema / properties / qa_status / enum

Added value: +[
+  "",
+  "pending",
+  "in_progress",
+  "ready",
+  "blocked"
+]

addedInput schema / properties / version_type / enum

Added value: +[
+  "",
+  "raw",
+  "qa_cleaned",
+  "training"
+]

First observedv0.1.0May 29, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations declare destructiveHint=true, idempotentHint=false, readOnlyHint=false, and the description adds real value beyond them: client-side refusal of malformed file_url forms, an API 400 when the path is outside users/{userId}/, the fact that qa_status is purely informational, and the training-job constraint on raw versions.

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 two bolded mode headers and organized by required fields per branch, so the agent can parse it quickly. The s3Key cross-referencing (complete_upload / get_dataset_version) is somewhat dense but each sentence carries operational content.

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?

For a 7-param mutation tool with an output schema, everything an agent needs to invoke correctly is present: mode selection, required fields, input format constraints, failure conditions, and downstream effects on training. Return values are legitimately omitted given the output schema.

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 load, and it documents five of seven parameters including enum values (version_type raw|qa_cleaned|training, qa_status pending|in_progress|ready|blocked) and the two accepted file_url formats. Only project_id and dataset_id scoping are left undescribed, which is a minor gap.

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 and cleanly splits the two modes (create registering an S3 file as a version vs update changing qa_status). It names related siblings (complete_upload, get_dataset_version, submit_training_job) so an agent can place it in the workflow without opening schemas.

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?

Gives explicit conditions for choosing each mode (required field sets differ) and a concrete routing rule for submit_training_job with dataset_version_id. It stops short of distinguishing this tool from the sibling update_dataset, so the branch selection is clear but the sibling boundary is left implicit.

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