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Get credit balance

get_credit_balance
Read-only

Get the organization's credit balance: monthly credits (reset each billing period) and pack credits (purchased, never expire). Audits spend credits pay-as-you-go while they run; run_audit shows an upper-bound estimate before starting. An audit costs 50 credits plus 2 per audited page, however the page is fetched, plus 1 per distinct external link the cloud verifies when external link checking is on (a link it cannot verify costs nothing). A balance under 52 (a one-page audit) cannot start one, and a balance that covers fewer pages than requested starts a smaller audit. Read-only.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +{}
  2. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint annotation by disclosing the credit lifecycle (monthly resets, pack credits never expire), the pay-as-you-go spend model, the exact audit cost formula, and the failure threshold (under 52 credits cannot start an audit). 'Read-only' restates the annotation but nothing contradicts it.

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?

Front-loads what the tool returns before covering spend mechanics and thresholds, and every sentence carries decision-relevant information. The detailed cost formula is arguably closer to run_audit's domain, making the paragraph slightly heavier than a pure balance getter needs, but it is dense rather than padding.

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?

With no output schema, the description carries the burden of describing return values and does so by naming the two credit categories. Combined with the spend/threshold context, an agent has everything needed to interpret and act on the result.

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 takes zero parameters, so per the rubric the baseline is 4; there is no per-parameter semantics to document and the description correctly adds none.

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?

States a specific verb and resource ('Get the organization's credit balance') and immediately decomposes the resource into its two distinct kinds (monthly credits vs. pack credits), which also separates it conceptually from the sibling list_credit_transactions that reports history rather than current state.

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?

Provides clear context tying the balance to audit spending and explicitly points at run_audit as the tool that shows an upper-bound estimate before starting. It stops short of an explicit 'call this when/do not call this when' statement, so no exclusions are given, but the decision context is well established.

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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