Plutus

list_cost_sources

Read-onlyIdempotent

List the cost-source providers (AWS, GCP, Anthropic, etc.) enabled for this account. Each row's cost_basis says where that provider's money figures come from: "invoiced" = amounts the provider actually charged; "estimated_from_provider_rates" = an estimate built from the provider's own published rates, because it exposes no historical billing API; "estimated_from_static_rates" = an estimate built from a list-rate card Plutus maintains, which will not reflect rates the customer negotiated. Qualify any total that includes an estimated source rather than reporting it as billed spend.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations by explaining the semantics of each possible cost_basis value, the limitations of estimate sources (no historical billing API, static rates ignoring negotiated discounts), and instructing the agent to qualify estimated totals rather than presenting them as billed spend.

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?

The description is efficiently structured: the first sentence states the primary action, and the second adds only essential detail about the cost_basis field. Every sentence contributes meaningfully, with no filler or repetition of annotation values.

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 list tool with annotations already covering read-only/idempotent behavior, the description fully explains the domain-specific distinctions that matter when interpreting results. Nothing essential 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?

This tool takes no parameters, so there are no parameter semantics to explain. The description correctly focuses on output semantics instead, which is the only meaningful guidance an agent needs here.

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 names a specific verb (List), a specific resource (cost-source providers enabled for this account), and examples (AWS, GCP, Anthropic). It clearly distinguishes this from sibling list_* tools by focusing on providers rather than tags, alerts, budgets, or other 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 conveys when to use it: when you need the enabled cost-source providers and their cost_basis meanings. It does not explicitly name alternatives or draw exclusion boundaries, but the provider-specific resource is unambiguous and no contradictory guidance is given.

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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