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
`id`	Yes	Widget ID (from list_widgets).
`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.
`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. A failure comes back as an error result instead.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 18, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "data": {
+      "additionalProperties": {},
+      "description": "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.",
+      "type": "object"
+    },
+    "success": {
+      "description": "True when the call succeeded. A failure comes back as an error result instead.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "success"
+  ],
+  "type": "object"
+}

First observedSep 18, 2026

TDQS

A3.7/5.0

Behavior4/5

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

The description adds meaningful behavioral context beyond the sparse annotations: 'the series is re-fetched in the background' and 'a dataset has no fixed catalog' explain side effects and data model nuances. It doesn't contradict annotations (readOnlyHint=false, destructiveHint=false).

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 sentences, front-loaded with the primary action, and the supplementary context (provenance and re-fetch) is placed second. No wasted words.

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?

The presence of an output schema and fully documented parameters reduces the burden on the description. However, for an update tool, it doesn't state whether omitted fields remain unchanged or get reset—a critical gap. The main workflow and data sourcing are covered, but the update semantics are ambiguous.

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 coverage is 100% with detailed descriptions and examples for all properties. The description adds sourcing provenance for ids and column names, but this is auxiliary rather than necessary—the schema already documents parameter semantics. Baseline 3 is appropriate.

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?

The description uses a specific verb ('point') and resource ('Custom Data widget') and explains the action ('choose its columns'), which clearly distinguishes it from generic data widgets. It doesn't explicitly name sibling tools, so it misses the extra differentiation credit, but the action is unambiguous.

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

Usage Guidelines3/5

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

It tells the agent where to source dataset IDs and column names (from list_custom_data and get_custom_data), which is helpful usage context. However, it doesn't explicitly contrast with add_custom_data_widget or update_data_widget, nor state conditions for when not to use this tool.

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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