Plutus

list_data_health

Read-onlyIdempotent

List sync health for every cost/event/usage connection this account has — last synced time, next scheduled sync, the outcome of its most recent run, and is_overdue (no successful sync within twice the connection's own effective sync interval). status is only ever set on a successful sync and is never flipped back on failure, so is_overdue — not status — is the real signal that a connection has gone stale. Check this before asserting that recent cost or usage data is complete, especially right before calling query_costs or query_usage for a very recent date range. Mirrors GET /api/accounts/:accountId/data-health.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, the description reveals important behavior: status is only set on successful syncs and never flipped back on failure, so is_overdue is the real staleness signal. This is exactly the kind of non-obvious runtime behavior agents need.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, each earning its place: the core purpose, the critical status/is_overdue nuance, and the concrete use case. The API endpoint reference is a useful bonus and does not bloat the description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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 is complete: it states what is listed, which fields are meaningful, how to interpret the key signal, when to call it, and even the REST endpoint it mirrors. Nothing essential for correct invocation or interpretation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema is fully covered, so there is no parameter gap to compensate for. The description still adds useful context by clarifying that the listing is account-scoped and that is_overdue is a computed signal based on the connection's effective sync interval.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'List sync health for every cost/event/usage connection this account has.' It enumerates the returned fields and clearly distinguishes this health-monitoring tool from the many sibling list_* and query_* tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use the tool: check it before asserting recent cost/usage data is complete, especially before calling query_costs or query_usage for a recent date range. It also tells agents to rely on is_overdue rather than status, which prevents misinterpretation.

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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TDQS

A3.9/5.0

Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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.

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