get_dashboardGet one dashboard in full: its settings, theme, publishing state, every view, and every widget on each view including the widget queries. This is the fastest way to understand an existing dashboard before changing it.
| Name | Required | Description | Default |
|---|---|---|---|
| dashboardId | Yes | Dashboard id. | |
| includeWidgets | No | Include every widget on every view. Defaults to true. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context by specifying the full response scope: settings, theme, publishing state, every view, every widget, and widget queries. There is no contradiction with 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence front-loads the tool's full scope, and the second provides a concise usage rationale. Every word earns its place.
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?
Even without an output schema, the description enumerates the major return contents in detail: settings, theme, publishing state, views, widgets, and queries. Combined with full input-schema coverage and supportive annotations, an agent has enough context to invoke the tool appropriately.
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%, so both dashboardId and includeWidgets are already documented in the input schema. The description's mention of 'every view and every widget' aligns with includeWidgets' default of true but adds no new parameter-level semantics beyond the schema.
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 and resource: 'Get one dashboard in full', then enumerates exactly what is included: settings, theme, publishing state, views, widgets, and widget queries. This clearly differentiates it from sibling tools like list_dashboards (which lists many dashboards) and update_dashboard (which changes a dashboard).
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?
The second sentence, 'This is the fastest way to understand an existing dashboard before changing it', gives a clear decision context for using the tool. It does not explicitly name alternatives or state when not to use it, so it stops short of a full 5.
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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Most tools target a distinct resource+action pair, and descriptions explicitly cross-reference related tools (e.g. update_dashboard points to set_dashboard_theme and publish_dashboard). A few pairs remain close enough to cause hesitation—set_widget_layout vs update_widget's position parameter, and get_started vs get_platform_overview—but their descriptions do separate them.
Tool names consistently follow verb_noun snake_case with clear resource nouns like customer, dashboard, widget, and data_source. Minor deviations exist between add_* and create_* for creation operations, and set_* versus update_* for mutations, but the overall pattern is still predictable.
With 42 tools, the surface is much larger than the 16-25 range that already feels heavy, and several onboarding/catalog helpers (get_started, get_platform_overview, list_plans_and_limits, list_supported_data_connectors, list_widget_types) add to the count. Each tool has a distinct job, but the set would benefit from consolidation or splitting into focused sub-servers.
Core lifecycle coverage is strong: dashboards, views, widgets, data sources, customers, and publishing all have create/read/update/delete where relevant, plus test/preview/validation tools. Obvious gaps are customer-user management beyond create/delete (no password reset/update) and no direct way to move a widget between views, but agents can work around these.