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superset_dataset_update

Update Apache Superset datasets: modify table name, SQL, description, columns, or metrics. Only changed fields are needed; replacing all columns requires confirmation.

Instructions

Update a dataset. Only pass the fields you want to change.

Args: dataset_id: ID of the dataset to update. table_name: New table/dataset name. sql: New SQL query (only for virtual datasets). description: Dataset description (displayed in Superset UI). columns: JSON string describing columns. CRITICAL: passing columns REPLACES ALL dataset columns! To update a single column (e.g. verbose_name), pass ALL columns with their IDs. After an error, dataset_refresh_schema will restore columns from SQL. Format: [{"id": 123, "column_name": "id", "type": "INTEGER", ...}] metrics: JSON string describing metrics. Format: [{"metric_name": "count", "expression": "COUNT(*)", "metric_type": "count"}] confirm_columns_replace: Confirmation for replacing ALL columns (REQUIRED when passing columns). always_filter_main_dttm: Force the dataset's main datetime filter on/off. Superset resets this flag to false on any PUT that omits it, which breaks dashboard time filters when only columns/metrics are updated. Pass an explicit value to set it; leave it None and the current value is preserved automatically whenever columns are replaced.

Returns: JSON string with the updated dataset details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNo
columnsNo
metricsNo
dataset_idYes
table_nameNo
descriptionNo
always_filter_main_dttmNo
confirm_columns_replaceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries the full burden and excels: it discloses the destructive behavior of columns replacement, the required confirm_columns_replace gate, the Superset flag reset gotcha, and the recovery path via dataset_refresh_schema. This is precisely the safety-critical context an agent needs.

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?

The description is longer than a simple one-liner, but every sentence adds value—warnings, formats, and gotchas are included. It is well-structured with clear Args/Returns sections and front-loaded purpose. The only slight detractor is the length, but it is justified by the tool's complexity.

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?

The tool has 8 parameters, complex side effects, and specific edge cases. The description covers every parameter, explains return format, and provides essential operational guidance. Given the rich sibling context and no output schema details in the prompt, this description is complete enough for safe and correct invocation.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates fully. Each parameter is explained beyond its schema type: columns includes a format example and the critical replacement behavior, metrics includes a format, and always_filter_main_dttm includes detailed semantics about omission and preservation. This far exceeds schema info.

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?

The description opens with a clear verb+resource statement: 'Update a dataset.' followed by the usage rule 'Only pass the fields you want to change.' This unambiguously distinguishes it from the create/delete/refresh siblings in the toolset.

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?

The description gives clear context on how to perform partial updates and includes critical warnings about replacing columns and the always_filter_main_dttm flag. However, it does not explicitly mention alternatives (e.g., 'use superset_dataset_create for new datasets'), though the sibling names make this obvious.

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

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