dbx-mcp

Manage AI/BI dashboards

manage_dashboard

Destructive

Create, get, list, update, delete, publish, or unpublish Databricks AI/BI (Lakeview) dashboards, including draft edits, trash recovery, and published versions.

Instructions

Manage AI/BI (Lakeview) dashboards. Actions: create (display_name, optional parent_path, warehouse_id, serialized_dashboard), get, list (show_trashed), update (draft fields; etag for optimistic concurrency), delete (moves to trash; recoverable), publish (embed_credentials, warehouse_id), unpublish, get_published. Created dashboards are tracked in the project manifest.

Safety classification: depends on input (DESTRUCTIVE, READ_ONLY, SECURITY_SENSITIVE, WRITE).

Input Schema

TableJSON Schema

Name	Required	Description
`etag`	No	update: etag from get, to fail if the draft changed meanwhile.
`action`	Yes	create \| get \| list \| update (draft) \| delete (move to trash) \| publish \| unpublish \| get_published
`confirm`	No	Set to true ONLY after the user has reviewed the plan returned by a previous call with status 'confirmation_required'. Required for destructive/security-sensitive actions.
`dry_run`	No	If true, validate and return the planned change without executing it.
`page_size`	No	Max items to return (server caps this).
`page_token`	No	next_page_token from a previous response.
`parent_path`	No	create: workspace folder for the dashboard, e.g. /Users/me@x.com/dashboards.
`dashboard_id`	No	Dashboard id (all actions except create/list).
`display_name`	No	create/update: dashboard name.
`show_trashed`	No	list: include dashboards in the trash.
`warehouse_id`	No	create/update: SQL warehouse for the draft; publish: override warehouse.
`dataset_schema`	No	create/update: default schema for all datasets.
`dataset_catalog`	No	create/update: default catalog for all datasets.
`embed_credentials`	No	publish: run viewers' queries with the publisher's credentials (SECURITY_SENSITIVE; requires confirm). Default false: viewers use their own credentials.
`serialized_dashboard`	No	create/update: the dashboard definition (JSON string or object, as exported by Databricks).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`page`	No
`plan`	No
`tool`	Yes
`action`	No
`safety`	No
`status`	No		success
`summary`	Yes
`warnings`	No
`next_steps`	No	Suggested follow-up calls.
`request_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 3, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=true), the description adds real behavioral context: delete moves to trash and is recoverable, update uses etag for optimistic concurrency, publish with embed_credentials is SECURITY_SENSITIVE and requires confirm, and created dashboards are tracked in the project manifest. It also clarifies the safety classification varies by action rather than being fixed.

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?

Front-loads the resource and then a compact action list with parenthetical arguments, followed by two short standalone notes. Dense but every clause carries information; only the safety-classification restatement is somewhat redundant with annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 15-parameter, 8-action tool with an output schema and annotations present, the description covers the action surface, the recoverability of delete, the concurrency mechanism, and credential sensitivity. Pagination and the confirmation/dry_run workflow are left to the schema, which is reasonable given full coverage.

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 description coverage is 100%, so every parameter is already documented in the schema. The description maps parameters to actions (e.g. parent_path and serialized_dashboard for create, etag for update), which adds light orientation but nothing beyond the schema's own descriptions. Baseline 3 is appropriate.

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?

States a specific verb (Manage) and resource (AI/BI Lakeview dashboards) and enumerates the eight discrete actions with their key arguments. An agent can identify this as the dashboard lifecycle tool and distinguish it from siblings like manage_metric_views or manage_workspace.

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?

The per-action argument mapping implies what each action does, which aids action selection, but there is no explicit guidance on when to use this tool versus siblings, nor any when-not conditions. Usage is inferable but not spelled out.

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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