list_supported_data_connectorsList every data source type CustomerDashboard can connect to, the settings each one needs, whether widgets can query it, and whether its schema can be introspected. No sign-in required.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 declare this to be a safe, idempotent, read-only operation. The description adds valuable context beyond the annotations by stating that no sign-in is required and clarifying the scope of the response (supported types only, with settings and capability flags). This is meaningful behavioral disclosure for an unauthenticated listing operation.
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?
The entire description is one tight sentence that front-loads the primary action and resource, then efficiently enumerates the returned information fields. The final note about sign-in is the only extra piece and it earns its place by clarifying access requirements.
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 zero-parameter, read-only listing tool with no output schema, the description fully specifies the scope of the list and the attributes included for each connector. An agent can correctly set expectations for the response and knows no authentication is required, leaving no critical gap for invoking this tool.
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?
The tool has zero parameters, so the description correctly avoids any parameter-specific guidance. The claim that it lists 'every data source type' and the details included help the agent understand what kind of data it will receive, which is the only semantic context needed for a no-parameter tool.
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 ('List') and a clear resource ('every data source type CustomerDashboard can connect to'), and goes further by enumerating exactly what is returned for each connector: settings, widget queryability, and schema introspection. This clearly distinguishes it from sibling tools like list_data_sources or get_data_source_schema, which operate on configured data sources rather than supported types.
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 description clearly implies when to use the tool: any time an agent needs to know which data source types are available and what configuration each requires. It also notes that no sign-in is required, which is useful access context. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of a 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.