WhitePact

Check AI Spending Against Budget

rai_budget_check

Read-onlyIdempotent

Check AI spending against monthly budget limits to prevent overruns. Returns consumption percentage, alert status, team and model breakdowns, and projected month-end spend.

Instructions

Evaluate current AI spending against monthly budget limits. Returns consumption percentage, alert status, per-team and per-model breakdown, and projected month-end spend. Used by LLMOps Engineers and Finance to prevent budget overruns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "total_spent_usd": "<total_spent_usd>"
    },
    "name": "rai_budget_check"
  }
}

Input Schema

Name	Required	Description
`days_elapsed`	No
`days_in_month`	No
`team_breakdown`	No	team_name → USD spent
`model_breakdown`	No	model_name → USD spent
`total_spent_usd`	Yes
`monthly_limit_usd`	No
`alert_threshold_pct`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.6Sep 11, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description usefully discloses the computation outputs (consumption %, alert status, projections), which matters because there is no output schema, but it never clarifies that the caller must supply the raw spend figures rather than the tool fetching them—a meaningful behavioral gap.

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

Conciseness4/5

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

Three tight sentences, front-loaded with the core action and then the return values. The audience clause is the only slightly expendable part, but nothing is padded.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description's enumeration of returned metrics is genuinely necessary and present. However, the input-side story is incomplete: seven parameters, undocumented semantics for alert_threshold_pct and the day-count pair, and no note about required spend inputs.

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

Parameters2/5

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

Schema description coverage is only 29% and the description explains none of the seven parameters. In particular, alert_threshold_pct (default 0.8, max 1) and monthly_limit_usd are ambiguous—fraction vs. percentage, default limit source—and the description does nothing to compensate.

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

Purpose4/5

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

States a specific verb and resource: 'Evaluate current AI spending against monthly budget limits', and enumerates the computed outputs. It is clear what the tool does, though it does not distinguish itself from nearby siblings like rai_cost_estimate.

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

Usage Guidelines3/5

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

The phrase 'Used by LLMOps Engineers and Finance to prevent budget overruns' implies a monitoring use case, but there is no explicit when-to-use, no prerequisites, and no named alternative (e.g. rai_cost_estimate) for estimating rather than checking spend.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.