list_plans_and_limitsList CustomerDashboard's subscription plans, their monthly price and the resource limits each one allows. Useful for explaining why a create operation was blocked. 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?
The annotations already cover read-only/idempotent/non-destructive behavior, and the description adds useful context beyond those annotations: no sign-in is required and the response exposes pricing and limit configuration. It does not contradict any annotation.
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?
Three compact sentences: the primary action and result, the motivating use case, and the auth requirement. Every sentence earns its place and the main operation is front-loaded.
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 no-argument read-only tool with rich annotations, this is complete: it states what is returned, why an agent would call it, and the auth expectation. No output schema exists, but the description itself conveys the output content (plans, prices, limits).
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?
With zero parameters, the schema leaves no ambiguity and the description does not need to document parameter meaning. The 0-parameter baseline of 4 applies; the description's content about returned data is helpful but not parameter-related.
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 names a specific verb ('List'), a precise resource ('CustomerDashboard's subscription plans'), and the fields returned (monthly price, resource limits). This clearly distinguishes it from sibling list tools like list_customers or list_dashboards, which target different entities.
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?
It gives a concrete use case ('explaining why a create operation was blocked') that tells an agent when to call it. There is no direct sibling alternative for plans, so explicit exclusions are unnecessary; a small 'when not to use' note would have made it fully explicit.
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.