List recent alert delivery history for this account (did an alert actually fire, and did it send successfully) — the same data behind the Alerts page's delivery heatmap. Does not include the delivery error text. Mirrors GET /api/accounts/:accountId/alert-deliveries.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows, most recent first (default: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable behavioral details: it only covers recent history, does not include delivery error text, and corresponds to a specific API endpoint. This goes beyond what annotations provide.
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?
Two sentences with no wasted words. The core purpose is front-loaded, followed by a useful exclusion and an API reference that aids discoverability. Every sentence earns its place.
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 list operation with one optional parameter and rich annotations, the description is fully sufficient. It explains what the tool does, what data it surfaces, a key exclusion, and the underlying endpoint, so an agent has everything needed to call it correctly.
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 100%, and the only parameter, limit, is fully described in the schema with default and ordering semantics. The tool description adds no parameter-specific details, which is acceptable given the schema already carries the full burden.
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?
Description states a specific verb and resource: list recent alert delivery history. The parenthetical clarifies what question it answers (did it fire, did it send successfully), and 'deliveries' clearly distinguishes it from sibling tools like list_alert_channels and list_alert_subscriptions.
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?
Provides clear context: use this to inspect whether alerts actually fired and sent successfully, mirroring the Alerts page's delivery heatmap. It does not explicitly name alternatives or list when-not-to-use conditions, but the purpose is distinct enough that an agent can infer appropriate use.
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.