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Get generation credit allowance

webanto_get_usage
Read-onlyIdempotent

Inspect generation and knowledge-search credit estimates, organization allowance and this key's actual period usage/cap. Optional operation_id returns known primary generation credits or zero for local draft edits; child jobs are excluded and absent ledger evidence stays unknown. Operation lookup also requires jobs:read and originating-key ownership. No charge; credits are not dollars. Allowance is rechecked at execution.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "webanto_get_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
operation_idNoOptional saved operation id started by this key; also requires jobs:read.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds genuinely new behavior: no charge is incurred, credits are not dollars, allowance is rechecked at execution, and operation lookup requires jobs:read plus originating-key ownership. It also discloses edge-case semantics (child jobs excluded, missing ledger evidence reported as unknown).

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-loaded with the core purpose, then the optional-parameter behavior, then caveats. Dense but every clause carries information; the telegraphic phrasing ('absent ledger evidence stays unknown') is slightly compressed but not wasteful.

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?

An output schema exists so return values need not be explained, and the description still covers auth prerequisites, exclusion rules, unknown-evidence handling, and the execution-time recheck. For a single-optional-parameter read tool, nothing an agent needs to call it correctly 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?

Schema coverage is 100% and the schema already documents operation_id, yet the description adds real meaning: it returns known primary generation credits, returns zero for local draft edits, excludes child jobs, and requires jobs:read and originating-key ownership. That goes beyond the schema's terse field note.

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 ('Inspect') and a precise resource set: generation and knowledge-search credit estimates, organization allowance, and this key's period usage/cap. No sibling tool covers credits or usage, so the agent can distinguish it immediately from the get_* family.

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 description explains what the optional operation_id path yields and its exclusions, which implies when to pass it, but it never states the primary when-to-use case (e.g. check allowance before starting a generation) or names an alternative. Usage is inferable rather than explicit.

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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