List the distinct values seen for a faceted event metadata field over a date range — currently just "repo" (every GitHub repo with at least one event), the same facet the Event Explorer's repo picker uses. Useful for discovering query_events' meta_repo filter values before calling it. Mirrors GET /api/events/facets.
| Name | Required | Description | Default |
|---|---|---|---|
| attr | Yes | ||
| end_date | No | ||
| source_id | No | ||
| start_date | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true and destructiveHint false. The description adds useful behavior beyond that: currently only the 'repo' facet is supported, results mirror GET /api/events/facets, and it operates over a date range. It does not detail response shape or pagination, but the read-only annotation lowers that burden.
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 sentences with no filler: it says what the tool lists, the current facet constraint, and the intended use with query_events. The mention of the mirrored API endpoint is a compact, useful addition rather than repetition.
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 simple read-only facet listing tool, the description is largely complete: it names the supported facet, clarifies the date-range context, and explains how to use it with query_events. The absence of an output schema is mitigated by the self-explanatory list operation, although source_id semantics and return shape remain undocumented.
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 0%, so the description must compensate. It clarifies that attr currently means 'repo' and connects this to query_events' meta_repo filter, and it suggests start_date/end_date bound the date range. However, source_id is not explained, and date format or requiredness is left implicit.
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 and resource: list distinct values for a faceted event metadata field, with the concrete current example 'repo'. It clearly distinguishes itself from sibling list_* tools by tying it to event facets, Event Explorer, and query_events.
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 explicitly says the tool is useful for discovering query_events' meta_repo filter values before calling query_events, which is a clear when-to-use signal. It does not enumerate alternatives or exclusions, but tthe sibing context makes those mostly unnecessary.
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.