ARADIA | sovereign agentic systems

execute_procurement_order

Submits a binding procurement order for physical AI hardware. Returns actionable programmatic payment parameters for immediate autonomous 100% upfront settlement via BTCPay Crypto or Stripe MPP Fiat.

Input Schema

TableJSON Schema

Name	Required	Description
`quantity`	No	The number of appliance nodes to purchase.
`po_number`	No	Optional purchase order number for reference.
`sla_active`	No	Whether SLA is active for this order.
`sla_monthly`	No	Monthly SLA cost if active.
`customer_name`	No	Name of the AI Agent, Operator, or Administrator executing the order.
`hardware_tier`	Yes	The hardware tier being purchased.
`customer_email`	Yes	Email address of the purchasing administrator or target billing point.
`payment_method`	Yes	The preferred settlement method: (a) 'stripe_mpp' for fiat via Shared Payment Token (SPT), (b) 'btcpay_crypto' for raw on-chain BTC/LN/USDT JSON settlement, (c) 'btcpay_usdc_base' for USDC, (d) 'stripe_fiat' for Stripe Checkout URL, (e) 'stripe_invoice' for formal wire invoice.
`customer_company`	No	Company, DAO, or Organization name associated with this hardware deployment.
`partner_ref_code`	No	Optional 4-character referral code to route 10% commission to a registered partner.

Output Schema

TableJSON Schema

Name	Required	Description
`amount_usd`	No	Total purchase order amount in USD
`invoice_id`	No	Generated invoice or payment intent ID
`payment_url`	No	Settlement URL
`payment_method`	No	Chosen payment rail
`payment_details`	No	Raw destination addresses and amounts for crypto settlement

TDQS

A4.2/5.0

Behavior4/5

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

The description highlights 'binding' and '100% upfront settlement', which are materially important behavioral disclosures beyond the readOnlyHint=false and destructiveHint=false annotations. It does not fully spell out consequences like a hard purchase obligation, but 'binding' covers the key risk well enough.

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 dense sentences, no filler, and the core action is front-loaded: 'Submits a binding procurement order...' The payment outline is the single most valuable addition after the action itself.

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?

Given the 100% schema coverage, output schema, and annotations, the description covers the essential behavioral and financial context. A minor gap is that it does not clarify whether the order is only finalized after successful payment, but 'binding' and 'returns... payment parameters' strongly imply the order is the commitment.

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 description coverage is 100%, so the parameter descriptions already explain the properties. The tool description adds little to individual parameter semantics beyond framing the payment output, which is acceptable given the schema richness.

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 states a specific verb and resource: 'Submits a binding procurement order for physical AI hardware.' It also tells the agent that the tool returns payment parameters, making its purpose unmistakable and distinct from sibling tools like query_hardware_specs or calculate_roi.

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 makes the context clear: this is for actually placing a procurement order rather than researching specs or calculating ROI. It does not explicitly state when not to use it, but the binding language and payment focus make the intended usage obvious.

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

Disambiguation5/5

Each tool has a single, distinct purpose: partner onboarding, ROI calculation, hardware specification lookup, and procurement order execution. There is no meaningful overlap or ambiguity between them.

Naming Consistency4/5

Three tools follow a clean verb_noun snake_case pattern: calculate_roi, execute_procurement_order, query_hardware_specs. The aradia_ prefix on aradia_partner_onboard is a minor deviation, but the overall naming style remains predictable.

Tool Count4/5

Four tools form a coherent basic procurement and partner-onboarding workflow. The count is reasonable, though slightly small for a commercial system that might also benefit from order-status or partner-management tools.

Completeness3/5

The main pre-order workflow is covered: discover specs, calculate ROI, onboard as a partner, and execute procurement. However, there is no way to check order status, cancel or update orders, or manage partner lifecycle after onboarding, which are notable gaps.

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