CustomerDashboard

Preview a query

run_query_preview

Read-only

Run a query against a connected data source and return the first few rows, so you can confirm it works before putting it on a widget. Results are capped at 100 rows. If the query contains :customer_id you must also say which customer to preview as, using customerId or dashboardId.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Rows to return. Defaults to 25.
`query`	Yes	The SQL to run, or an A1 range for Google Sheets. Use :customer_id where a customer's own identifier belongs.
`customerId`	No	The id of a customer (from list_customers) to run the query as. Required when the query uses :customer_id, unless dashboardId is given.
`dashboardId`	No	A customer dashboard whose configured test customer should be used instead of a real customer. Only needed when the query uses :customer_id.
`dataSourceId`	Yes	Data source id.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive behavior, and the description adds the important 100-row cap plus the preview semantics. No contradiction with annotations exists, and the customer-context requirement is transparent.

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 three sentences with zero filler: it states the purpose, the result cap, and the key conditional requirement. Information is front-loaded and every sentence earns its place.

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?

The description covers the invocation context, return behavior, result size limits, and special customer-context handling. Combined with the fully described parameters and safety annotations, this is sufficient for reliable use, even without an output schema.

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 the baseline applies. The description restates the customerId/dashboardId conditional and the row cap, but it does not add new parameter semantics beyond what the schema already documents.

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: run a query against a connected data source and return the first few rows to confirm it works before placing it on a widget. This clearly distinguishes it from sibling tools like test_data_source_connection or get_data_source_schema by tying it to previewing query results.

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 explicitly tells the agent when to use the tool: before putting a query on a widget. It also gives clear conditional guidance for queries containing :customer_id, requiring customerId or dashboardId. It does not explicitly name sibling alternatives or exclusions, but the usage context is clear.

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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TDQS

A3.9/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources

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