oviond

Update custom data widget

update_custom_data_widget

Point a Custom Data widget at an uploaded CSV dataset and choose its columns. The dataset id comes from list_custom_data and the column names from get_custom_data — a dataset has no fixed catalog, so its own column names are the metrics and dimensions. The series is re-fetched in the background.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	No	Display name.
`chart`	No	Chart type.
`metrics`	No	Value columns to plot, e.g. [{ column: "Revenue", format: "currency" }] — names exactly as get_custom_data reports them.
`widget_id`	Yes	Widget ID (from list_widgets).
`dimensions`	No	Label column(s) to break down by, e.g. ["Channel"] — names exactly as get_custom_data reports them.
`custom_data_id`	No	Custom-data dataset id (from list_custom_data).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No	The widget as saved, now pointed at that dataset and its columns. The series is re-fetching in the background — read it with get_widget_data.
`success`	Yes	True when the call succeeded.

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedSep 25, 2026

removedInput schema / $schema

Removed value: -"http://json-schema.org/draft-07/schema#"

removedInput schema / properties / id

Removed value: -{
-  "description": "Widget ID (from list_widgets).",
-  "type": "string"
-}

addedInput schema / properties / widget_id

Added value: +{
+  "description": "Widget ID (from list_widgets).",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "id"
-]New value: +[
+  "widget_id"
+]

removedOutput schema / $schema

Removed value: -"http://json-schema.org/draft-07/schema#"

changedOutput schema / properties / success / description

Previous value: -"True when the call succeeded. A failure comes back as an error result instead."New value: +"True when the call succeeded."

First observedSep 18, 2026

TDQS

A4.5/5.0

Behavior4/5

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

The description discloses a non-obvious side effect: 'The series is re-fetched in the background.' This adds behavior beyond the annotations, which only indicate readOnlyHint=false and destructiveHint=false. It does not contradict the annotations.

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?

The description is compact: a clear opening statement of the operation, followed by the source of identifiers and a one-line behavioral note. Every sentence adds information and the most important action is front-loaded.

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?

Given that an output schema exists and annotations already signal mutation, the description covers the key remaining context: where to get the dataset id and column names, that columns are the metrics/dimensions, and that a background refetch occurs. Nothing essential for invoking the tool correctly is missing.

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 100%, so the baseline is 3, but the description adds meaningful semantic guidance: a dataset has no fixed catalog and its own column names serve as metrics and dimensions. This helps the agent correctly derive metrics and dimensions from get_custom_data instead of guessing field names.

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 states a specific action and resource: 'Point a Custom Data widget at an uploaded CSV dataset and choose its columns.' It clearly conveys that the tool reconfigures a widget's data source rather than merely restating the tool name, and it distinguishes the widget type from generic data widgets.

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 concrete usage context by telling the agent where to obtain required identifiers: 'The dataset id comes from list_custom_data and the column names from get_custom_data.' It does not explicitly contrast this tool with add_custom_data_widget or update_data_widget, so it stops short of full alternative-routing guidance.

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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