easydeploy-ai-mcp

Official

by EasyDeploy-AI

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Get dataset

get_dataset

Read-onlyIdempotent

Retrieve one dataset's details—name, description, type, timestamps—using project and dataset IDs for read-only reference or verification.

Instructions

Fetch one dataset by id (name, description, type, timestamps). Read-only.

Datasets are created via complete_upload (the upload flow). To rename a dataset or change its description, use update_dataset.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dataset_id`	Yes
`project_id`	Yes

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
- removedInput schema / properties / description
```
Removed value: -{
-  "default": "",
-  "type": "string"
-}
```
- removedInput schema / properties / name
```
Removed value: -{
-  "default": "",
-  "type": "string"
-}
```
First observedv0.1.0May 29, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, and the description's 'Read-only' is largely redundant with them. However, the lifecycle context (datasets originate from complete_upload, mutations live in update_dataset) is genuine behavioral context not present in the structured fields.

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?

Two short sentences, front-loaded with the core action and return fields; the routing note follows immediately. No filler.

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 need no elaboration, and the alternative-tool guidance is complete. The only shortfall is the absent parameter guidance for the two required ids.

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

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description only says 'by id', never mentioning that project_id is also required alongside dataset_id, nor any format expectations. With two required undocumented parameters, the description fails to compensate for the schema 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 ('Fetch one dataset by id') and enumerates the fields returned (name, description, type, timestamps), which cleanly separates it from list_datasets and get_dataset_version.

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?

Explicitly routes the agent: datasets are created via complete_upload, and renaming/description changes go to update_dataset. It tells the agent not just when to use this tool but which sibling to use instead for adjacent operations.

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