datalens_charts_wizard_deletePermanently delete a chart by ID, removing it from any dashboards that display it. This action cannot be undone; affected dashboards will still reference the missing chart.
Delete a chart; the dashboards that show it lose it.
The API has no way to bring it back, and a dashboard that shows it keeps naming its id (measured).
| Name | Required | Description | Default |
|---|---|---|---|
| chart_id | Yes | Chart id. |
Changes observed during successful MCP inspections.
v0.122.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=false, but the description adds real value beyond them: dashboards displaying the chart lose it, the dashboards continue to reference the deleted id, and deletion cannot be undone through the API. This is meaningful consequence disclosure. It stops short of stating permission requirements or failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, and the destructive effect is front-loaded. But the second sentence is garbled ('a dashboard that shows it keeps naming its id (measured)') and the parenthetical '(measured)' reads like an internal spec artifact rather than agent-usable guidance, hurting clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter delete with annotations covering the safety profile and no output schema, the description covers consequences adequately. It nevertheless omits permissions/auth requirements and any note on error conditions (e.g., deleting a chart still referenced by a dashboard), leaving gaps an agent may hit at call time.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single chart_id parameter, so the schema already carries parameter meaning. The description adds nothing about the id format, source, or validity, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb (Delete) and resource (a chart) and clarifies the effect on dashboards. However, it never distinguishes this wizard-chart delete from the closely named siblings datalens_charts_ql_delete and datalens_charts_editor_delete, which an agent could easily confuse it with.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It describes the consequence of deletion but gives no guidance on when to choose this tool over the ql/editor chart-delete variants, nor any prerequisite such as detaching the chart from dashboards first. The irreversibility statement is useful but is a warning, not usage routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.