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krishna-goje

quicksight-mcp

by krishna-goje

create_dataset

Destructive

Define a new QuickSight dataset from a SQL query against a chosen data source, using SPICE caching or direct query mode for live data.

Instructions

Create a new QuickSight dataset from a SQL query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesThe SQL query for the dataset. Must be valid SQL for the target data source (e.g., Snowflake, Redshift).
nameYesHuman-readable dataset name.
import_modeNo'SPICE' (cached, default) or 'DIRECT_QUERY' (live).SPICE
data_source_arnYesARN of the QuickSight data source to query. Find this in the QuickSight console or via AWS CLI.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

B3.1/5.0
Behavior2/5

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

The only annotation is destructiveHint=true, and the description adds nothing about behavior beyond the annotation: it does not say whether naming collisions fail, whether the data source must be reachable, what permissions are required, or whether a refresh follows creation. For a creation tool that mutates account state, that is a significant omission.

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?

A single, front-loaded sentence with no filler; the verb and resource come first. Nothing is wasted.

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

Completeness3/5

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 not be described, and all four parameters are documented in the schema. However, in a toolset of this size, a create tool should at least say when it applies and what it requires; the description is adequate but thin.

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?

Schema description coverage is 100%, and the schema itself explains SQL validity, import_mode semantics, and how to obtain the data_source_arn. The description contributes no additional parameter meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a new QuickSight dataset') plus the input source ('from a SQL query'), so the agent knows exactly what is produced. No sibling performs the same create-dataset action, but the description never distinguishes itself from adjacent tools like clone_analysis or backup_dataset.

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

Usage Guidelines2/5

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

There is no guidance on when to use this instead of update_dataset_definition, update_dataset_sql, or clone_analysis, and no prerequisites or preconditions are mentioned. The agent must infer usage purely from the name.

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