DPX — Institutional Cross-Border Settlement

compute.route

Route a task to the best available free AI model and run inference. DPX selects the model based on the task type (reasoning → DeepSeek R1, code → Llama 3.3 70B, multilingual → Qwen 2.5 72B, fast → Llama 3.1 8B), calls OpenRouter, and returns the completion. All models are free-tier — no token cost. Pay per call in USDC via x402. Use this when an agent needs to delegate a subtask to a language model without managing model selection or API keys.

Input Schema

TableJSON Schema

Name	Required	Description
`task`	Yes	Plain-language description of what the model should do. Used for model selection.
`messages`	No	Optional. Full message array in OpenAI format [{role, content}]. If omitted, task is sent as a user message.
`preferSpeed`	No	true = use the fastest available free model regardless of task type. Default false.

TDQS

A4.5/5.0

Behavior5/5

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

The description discloses key behavioral traits beyond the annotations: model selection logic (reasoning → DeepSeek R1, code → Llama 3.3 70B, etc.), free-tier pricing with no token cost, payment via x402 in USDC, and integration with OpenRouter. These details give the agent a clear model of side effects and costs, which the annotations (only openWorldHint=true) do not provide.

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?

The description is concise: four sentences, each adding distinctive value. It starts with the primary action, then details the routing logic, cost model, and a clear use-case sentence. There is no filler or repetition of schema content.

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?

Given the moderate complexity and lack of an output schema, the description covers the essential operational context: what the tool does, how models are chosen, cost (free-tier plus x402 payment), and what it returns ('returns the completion'). It is sufficiently complete for an agent to select and invoke the tool.

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?

The input schema has 100% coverage for the 3 parameters, each with a description. The tool description adds examples of model selection but does not add new meaning to the parameters beyond the schema. Per the rubric, baseline 3 is appropriate when schema coverage is high.

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 opens with a specific verb-resource pair: 'Route a task to the best available free AI model and run inference.' This clearly identifies the tool's function and scope. It also distinguishes itself from sibling tools like compute.cost and compute.models by focusing on executing inference rather than just listing models or costs.

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 includes an explicit recommendation: 'Use this when an agent needs to delegate a subtask to a language model without managing model selection or API keys.' This clearly states when to use the tool. However, it does not mention when not to use it or offer alternative tools, so it falls short of a 5.

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

A4/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, especially within their domains (e.g., analytics, compliance, ESG, forecasting). However, a few tools like route and stability.stablecoin_route or settlement.quote and fx.cost_certainty may cause confusion despite distinct descriptions, and the large number of intelligence tools (cascade, aftershock, contagion, etc.) could lead to misselection without careful reading.

Naming Consistency3/5

Naming follows a domain prefix pattern (e.g., agent.kya_register, settlement.quote, esg.score), which provides some structure. However, inconsistencies exist: some tools use underscores (batch_settle, flow_check), others are single words (route), and the mix of verb_noun and noun_verb styles (e.g., compliance.pep_screen vs market.fx) reduces predictability.

Tool Count3/5

At 71 tools, the server is very broad in scope, covering compliance, ESG, forecasting, intelligence, treasury management, and more. While each tool seems justified for the complex institutional domain, the sheer number may overwhelm agents and makes the set feel bloated. A more focused scope or tighter tool grouping would improve appropriateness.

Completeness4/5

The tool surface is remarkably comprehensive for cross-border settlement, covering end-to-end workflow from quoting, FX analysis, compliance screening, ESG scoring, forecasting, and multiple payment rails (Mercury, Ramp, SWIFT). Minor gaps exist (e.g., no tool to update a settlement after execution), but core operations are well-covered, and the addition of integration and audit trails enhances completeness.

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