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preapprove_spend

Request one human approval for a batch of image/video generations. Use the returned approval_id to authorize each call up to approved dollars and call count for 3 hours.

Instructions

Get one human approval for a whole batch. Returns an approval_id to pass to each generate call; it covers up to the approved dollars and number of calls for 3 hours.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoWhat the batch is for
itemsNo
approval_idNoConfirm a pending batch, only after the user said yes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the human-in-the-loop requirement, the spend scope ('up to the approved dollars and number of calls'), and a concrete 3-hour validity window. It leaves gaps around idempotency, what happens on expiry, and whether calling it commits money, but the expiry and coverage-cap disclosures are exactly the behavioral traits an agent needs for a spend tool.

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?

Two compact sentences, front-loaded with the core action and followed by the mechanism and constraint. Every clause earns its place.

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

Completeness4/5

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. For a 3-param approval tool, the description covers the two-phase flow (request then confirm) and the validity window. It could say more about failure/expiry behavior and its relationship to estimate_cost, but it is close to complete.

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

Parameters3/5

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

Schema coverage is 67% and the schema itself documents the PlanItem fields well. The description enriches approval_id semantics by tying it to downstream generate calls, which is genuinely useful. It adds nothing for 'note' or 'items', so it sits at the baseline for a partially documented schema.

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+resource (get one human approval for a whole batch) and immediately explains what the tool produces (an approval_id). It is clearly distinguishable from the generate_* siblings because it is an approval/pre-flight step. However, it does not explicitly differentiate itself from the adjacent spend_status or estimate_cost siblings, which also belong to the pre-spend flow.

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?

Usage is implied rather than stated: the reader infers they should call this before generate calls and then reuse the approval_id. The approval_id parameter adds a strong conditional ('only after the user said yes') that clarifies the confirm-phase. But there is no explicit when/when-not guidance or comparison to estimate_cost, which an agent would plausibly confuse this with.

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