List the distinct values seen for a given dimension (service, region, linked_account, usage_type, or operation) for one provider — useful for discovering filter values before calling query_costs.
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | ||
| dimension | Yes | ||
| start_date | No | ||
| cost_source_id | Yes | Provider id, e.g. "aws" — must be enabled for this account |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds scoping context ('distinct values ... for one provider') but does not disclose pagination, date-range behavior, or result limits. This is adequate given the annotations but not rich.
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 description is one sentence, front-loaded with the action and target, and includes a practical use case. No repetition of schema fields and no filler.
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 read-only list tool with rich annotations, the description plus schema covers core behavior, required parameters, and the shape of results ('distinct values'). It is missing explicit mention of optional date filtering and does not distinguish from list_usage_dimensions, but those are secondary gaps rather than blocking omissions.
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 only 25%, with only cost_source_id documented. The description partly clarifies dimension via examples and cost_source_id via 'one provider', but it does not explain the optional start_date/end_date parameters, and it does not compensate for the sparsely documented 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 ('List') and resource ('distinct values ... for a given dimension'), includes provider scoping, and connects to a concrete use case. However, it does not differentiate from the similar sibling list_usage_dimensions, and it lists only five example dimensions while the schema enum allows ten.
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 explicitly says the tool is 'useful for discovering filter values before calling query_costs', which gives clear when-to-use guidance. It does not mention when not to use it or name alternative tools, so it stops short of a full exclusion-based comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are organized by resource (budgets, alerts, anomalies, dashboards, cost tags, recommendations), so most are clearly separable. The cost-tag cluster and the dimension/facet listers are the places where an agent could misselect by name, though descriptions resolve the ambiguity.
All tools use snake_case verb_noun names with a clear convention: get_ fetches specific items, list_ enumerates collections, and query_ runs time-bucketed or analytical queries. The pattern holds across all 29 tools with no camelCase or mixed verb styles.
29 tools is well past the typical 3–15 sweet spot and even past the 16–25 heavy band, so the surface feels sprawling despite having few duplicates. Each tool maps to a distinct endpoint, but the sheer number makes it a heavy set for an agent to select from.
The read-side is strong: costs, usage, tags, budgets, alerts, anomalies, dashboards, recommendations, and data health are all queryable. However, the surface is almost entirely read-only, and descriptions reference absent tools like create_budget, create_alert_subscription, create_dashboard, set_dashboard_widgets, and delete_dashboard, creating dead ends. That is a significant gap for a cost-management platform.