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Glama

CustomerDashboard

Create a widget

create_widget

Add a chart, table or KPI to a view. Call list_widget_types for the visualizationConfig each type needs, and run_query_preview first to confirm the query returns the columns you are about to reference. On a multi-customer dashboard the query must filter on :customer_id. Subject to your plan's widget limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSQL, or an A1 range for Google Sheets. On a customer dashboard include :customer_id, for example "WHERE customer_id = :customer_id".
titleYesWidget title.
layoutNoPosition and size on the 12-column grid. Omit on create and a free slot is chosen for you.
viewIdYesView id the widget belongs to.
dataSourceIdYesData source the query runs against.
visualizationTypeYes
visualizationConfigNoField mappings and display options for this visualization type. Call list_widget_types for the keys each type expects.
allowSharedAcrossAllCustomersNoOnly for customer dashboards. Set true, after the user explicitly confirms, to allow a query with no per-customer filter so every customer sees identical rows.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations are minimal (all false hints), so the description carries the burden and it delivers: it discloses the plan widget limit as a potential failure, the mandatory :customer_id filter on customer dashboards, and required precursor calls. No contradiction with annotations — readOnlyHint=false aligns with the write nature of "Add".

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?

Three sentences with zero filler: purpose is front-loaded first, prerequisites in the second sentence, and constraints in the third. 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?

For a tool with 8 parameters and no output schema, the description covers prerequisites, constraints, and limits well; the schema handles layout and allowSharedAcrossAllCustomers details. The one gap is that the return value (e.g., created widget id needed to chain into set_widget_layout or update_widget) is never stated, and there is no output schema to fill that void.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is high at 88%, so the baseline is 3. The description adds genuine cross-tool semantics beyond the schema: it maps visualizationConfig to list_widget_types and query to run_query_preview, telling the agent how to correctly populate two complex parameters.

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?

"Add a chart, table or KPI to a view" uses a specific verb plus resource enumeration and names the target container. This clearly differentiates create_widget from sibling create_dashboard and create_view without needing to open the schema.

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 gives explicit workflow guidance: call list_widget_types for visualizationConfig keys and run_query_preview first to validate query columns. It also states when the tool applies (multi-customer dashboards require :customer_id filtering) but stops short of explicitly naming when-not alternatives such as update_widget.

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