Plutus

list_scheduled_reports

Read-onlyIdempotent

List this account's scheduled cost digests — cadence, scope (whole account, one cost source, or one cost-allocation tag value), destination alert channel, and when the next one is due. next_period_label names the concrete date range the next digest will cover. Mirrors GET /api/accounts/:accountId/scheduled-reports. Creating/editing a scheduled report is not available over MCP.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description goes beyond annotations by stating account scoping, explaining the meaning of `next_period_label`, mirroring the REST endpoint, and explicitly noting that create/edit operations are not exposed.

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 tight, purposeful sentences. The first sentence front-loads the tool's purpose and return contents, the second clarifies a field's meaning, and the third gives endpoint context and an important limitation. No filler or redundancy.

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 parameterless, read-only list operation with no output schema, the description is complete: it states what is returned, the account-level scope, the meaning of a key field, and what is not possible over MCP. The annotations cover safety, so no additional behavioral disclaimers are needed.

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?

There are zero input parameters and the schema covers 100%, so the baseline of 4 applies. The description adds a small bonus by clarifying a response field (`next_period_label`), which is helpful despite there being no input parameters to document.

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 uses a specific verb ('List') and resource ('this account's scheduled cost digests'), and immediately clarifies the account scope. It also names the concrete fields returned, which clearly distinguishes this tool from the many list_* siblings that target different resources.

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

Usage Guidelines4/5

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

The description clearly implies when to use this tool: whenever the agent needs the account's scheduled report definitions. It also explicitly warns that creating/editing is not available over MCP, preventing misuse, though it does not name an explicit sibling alternative.

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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