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preset-mcp-datasets

Use Superset MCP tools for dataset inspection, semantic-layer querying, and virtual dataset creation. Use only for MCP tool workflows; do not use for direct API work.

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preset-io/agent-skills 12 1 forks Apache-2.0 Updated 14d ago

Part of preset-io/agent-skills — 28 skills

Install

skills CLI

npx skills add https://github.com/preset-io/agent-skills/tree/master/plugins/preset-mcp-skills/skills/preset-mcp-datasets

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install preset-io-agent-skills@llmmart

Git git clone https://github.com/preset-io/agent-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole preset-io/agent-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

preset-mcp-datasets

Use for dataset-centered MCP workflows.

Always

Use list_datasets and get_dataset_info for dataset discovery.
Respect permission-denied responses; do not work around them with chart, dashboard, SQL, or API calls.
Use saved metrics and dimensions from get_dataset_info; query_dataset accepts saved metrics only, not ad-hoc expressions. When no saved metric fits, compute the aggregate with execute_sql (route to preset-mcp-sqllab) instead of guessing metric names or stopping to ask.
Use query_dataset for semantic-layer data results.
Use create_virtual_dataset only when the user wants to save SQL as a chartable dataset.

Decision Rules

Metadata only: list_datasets, get_dataset_info.
Result table from metrics/dimensions: route through preset-mcp-data / query_dataset.
SQL-to-chartable-dataset workflow: create_virtual_dataset.
Visualization from dataset: route to preset-mcp-visualization.
Database discovery: use list_databases and get_database_info through discovery.

Workflow Order

Find the dataset with one list/search call (use a search filter and sufficient page_size); paginate or refine the filter only when the target is not in the returned page.
Inspect columns and metrics with one get_dataset_info call before data or chart workflows.
Query semantic-layer results only when the user asks for data.
Save virtual datasets only when persistence is requested.

Retrieve

Dataset workflows: references/dataset-workflows.md

Files (agent-skills)

references

dataset-workflows.md 731 B

# Dataset Workflows

| Goal | MCP Tool |
|---|---|
| Find datasets | `list_datasets` |
| Inspect columns and metrics | `get_dataset_info` |
| Query saved metrics and dimensions | `query_dataset` |
| Save SQL as a chartable virtual dataset | `create_virtual_dataset` |
| Find databases | `list_databases`, `get_database_info` |

For chart building, inspect `columns` and `metrics` first. Saved metrics should be referenced as saved metrics in chart configs, not reconstructed as raw column aggregations.

`query_dataset` accepts saved metrics only — not ad-hoc expressions. When a dataset has no saved metric for the requested aggregate, compute it with `execute_sql` through `preset-mcp-sqllab` instead of guessing metric names.

SKILL.md 1.8 KB

---
name: preset-mcp-datasets
description: Use Superset MCP tools for dataset inspection, semantic-layer querying, and virtual dataset creation. Use only for MCP tool workflows; do not use for direct API work.
---

# preset-mcp-datasets

Use for dataset-centered MCP workflows.

## Always

- Use `list_datasets` and `get_dataset_info` for dataset discovery.
- Respect permission-denied responses; do not work around them with chart, dashboard, SQL, or API calls.
- Use saved metrics and dimensions from `get_dataset_info`; `query_dataset` accepts saved metrics only, not ad-hoc expressions. When no saved metric fits, compute the aggregate with `execute_sql` (route to `preset-mcp-sqllab`) instead of guessing metric names or stopping to ask.
- Use `query_dataset` for semantic-layer data results.
- Use `create_virtual_dataset` only when the user wants to save SQL as a chartable dataset.

## Decision Rules

- Metadata only: `list_datasets`, `get_dataset_info`.
- Result table from metrics/dimensions: route through `preset-mcp-data` / `query_dataset`.
- SQL-to-chartable-dataset workflow: `create_virtual_dataset`.
- Visualization from dataset: route to `preset-mcp-visualization`.
- Database discovery: use `list_databases` and `get_database_info` through discovery.

## Workflow Order

1. Find the dataset with one list/search call (use a search filter and sufficient `page_size`); paginate or refine the filter only when the target is not in the returned page.
2. Inspect columns and metrics with one `get_dataset_info` call before data or chart workflows.
3. Query semantic-layer results only when the user asks for data.
4. Save virtual datasets only when persistence is requested.

## Retrieve

- Dataset workflows: [references/dataset-workflows.md](references/dataset-workflows.md)