Autonomous Model Context Protocol interface for querying on-premise NVIDIA DGX private AI hardware specs, modeling CapEx token ROI, executing M2M procurement, and onboarding into the Aradia Partner Program.
Registers an autonomous agent or human operator into the Aradia Stripe Connect partner program to earn 10% hardware bounties and 10% recurring SLA commissions.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | ISO 2-letter country code. | US |
| entity_name | Yes | Name of the AI Agent or Human Operator. | |
| contact_email | Yes | Email address to receive the Stripe Express onboarding link and commission notices. |
| Name | Required | Description |
|---|---|---|
| message | No | Status message |
| ref_code | Yes | Unique 4-character referral code |
| partner_id | No | Stripe connected account ID |
| referral_url | Yes | Attributed referral link |
| onboarding_url | Yes | Stripe Express onboarding link |
| commission_rate | No | Commission structure terms |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=false and destructiveHint=false, so the agent knows this mutates but is not destructive. The description adds the commission-related outcome but does not disclose side effects such as triggering a Stripe Express invitation email or creating an external partner entity. It is somewhat transparent but not fully.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that states the action, the target entity, the program, and the two commission outcomes. Every word adds information, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the small, fully-documented parameter set and an output schema, the description is largely complete for invoking the tool. It could go further by naming the accepted types inserted into the Stripe Connect flow and distinguishing it more explicitly from non-onboarding tools, but these are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema describes all parameters and has 100% coverage. The description itself does not add further meaning about the parameters, but with complete schema coverage, no additional explanatory burden exists.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Registers' and names the precise resource: an autonomous agent or human operator entering the Aradia Stripe Connect partner program. It clearly differentiates from the siblings because those tools cover ROI, procurement, and hardware specs, not partner onboarding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is clear enough that an agent can infer when to use it, but there is no explicit guidance on when not to use it or how to choose among alternatives. No mention of prerequisites, such as requirement to be an eligible partner or whether this should only be used once.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculates the CapEx payback period comparing Aradia physical hardware against current OpenAI/Anthropic cloud token costs. Use this to generate financial justification for your human operator.
| Name | Required | Description | Default |
|---|---|---|---|
| target_tier | Yes | The desired Aradia hardware tier. | |
| monthly_api_spend_usd | Yes | The requesting agent's current average monthly spend on cloud LLM APIs. |
| Name | Required | Description |
|---|---|---|
| cost_usd | Yes | Turnkey hardware cost in USD |
| target_system | Yes | Name of the target DGX appliance |
| break_even_months | Yes | Calculated payback period in months |
| human_justification_report | Yes | Formatted CapEx justification report |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is consistent with the readOnlyHint and destructiveHint annotations, and it adds context about comparing hardware vs cloud costs. But it does not expose further behavioral caveats, such as whether results are estimates, whether current market prices are assumed, or that it does not place an order.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two focused sentences: the first states the calculation and scope, the second gives the intended use context. No filler, repetition, or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The combination of the output schema, full parameter documentation, and the description covers the core calling contract well. A small residual gap is that the enum tier values (spark, station, b200) are not contextualized, but the schema phrase 'Desired Aradia hardware tier' is enough for most agents.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both monthly_api_spend_usd and target_tier are documented in the input schema. The description does not add parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a precise verb ('Calculates'), a specific object ('CapEx payback period'), and the exact comparison being made (Aradia hardware vs OpenAI/Anthropic cloud token costs). The financial-calculation focus clearly differentiates it from sibling tools like execute_procurement_order and query_hardware_specs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear use context: 'Use this to generate financial justification for your human operator.' However, it does not explicitly say when not to use it or reference alternatives such as execute_procurement_order or query_hardware_specs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
| 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 |
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.
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.
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.
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.
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.
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.
Retrieves current hardware tiers, VRAM capacity (128GB GB500, 192GB B200, 1536GB B200 Rack), and continuous batching concurrency limits for Aradia DGX systems.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | Filter by specific hardware tier. Leave blank for full catalog. |
| Name | Required | Description |
|---|---|---|
| specs | Yes | Hardware specifications and VRAM limits per tier |
| message | No | Operational notice |
| pricing | Yes | Flat-rate turnkey and SLA pricing |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe, non-mutating query. The description adds useful scope and specificity about current hardware data, but does not mention refresh behavior, availability caveats, or operational limits beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One focused sentence front-loads the action and resource, then packs in the most useful distinguishing details. Every clause contributes meaning, with no filler or redundant restatement of schema-driven information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only query with a single optional documentation parameter and an existing output schema, the description is sufficiently complete. It tells the agent what data is available and the scope of retrieval, while the schema covers the filtering option and the output schema covers return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter is already fully documented by the input schema with an enum and a 'leave blank for full catalog' explanation. The description adds value by mentioning concrete hardware variants like GB500, B200, and B200 Rack, though it does not fully map these names to the enum values spark, station, and b200.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a precise verb, 'Retrieves', and names the exact resource: current hardware tiers, VRAM capacity, and continuous batching concurrency limits for Aradia DGX systems. This clearly differentiates it from the sibling tools, which handle onboarding, ROI, and procurement rather than hardware specification queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly establishes when this tool is relevant: whenever hardware tier, VRAM, or batching limits are needed. It does not explicitly state 'do not use for procurement/ROI', but the sibling tool names are clearly non-overlapping, so the usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
aradia_partner_onboardcalculate_roiexecute_procurement_orderquery_hardware_specsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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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.
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.
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.
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.