DPX — Institutional Cross-Border Settlement

compute.cost

Read-onlyIdempotent

Get a model recommendation for a task type without running inference. Returns the best free model for the task, its strengths and speed tier, and a list of alternatives. Use this when an agent needs to select a model before committing to inference, or to surface model selection logic to a human. Free.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`task`	Yes	Description of the task — e.g. "summarize a financial document", "write Python code", "translate from French", "reason through a math problem".
`speed`	No	true = prefer fastest model over most capable. Default false.

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare read-only and non-destructive behavior. The description adds valuable behavioral context: it returns the 'best free model,' its 'strengths and speed tier,' and 'a list of alternatives,' and notes it is 'Free.' This clarifies the tool's output and cost implications beyond the annotations.

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 three concise sentences. Each sentence adds distinct value: what the tool does, what it returns, and when to use it. No redundant or filler 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 tool's simplicity (2 params, no output schema), the description is complete. It explains the returned data (strengths, speed tier, alternatives), the use case, and the 'free' aspect. No critical information appears missing.

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 100% for both parameters ('task' and 'speed'), so the schema already documents them. The description mentions 'task type' and output 'speed tier' but does not add new parameter-specific meaning beyond what the schema provides.

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 clearly states the tool's function: 'Get a model recommendation for a task type without running inference.' It specifies the resource (model recommendation) and the action (get), and differentiates from inference-related siblings like compute.route by emphasizing 'without running inference.'

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?

Explicit use cases are provided: 'Use this when an agent needs to select a model before committing to inference, or to surface model selection logic to a human.' This implies the tool is not for actually running inference, but it does not explicitly name sibling alternatives or state 'when not to use' beyond the purpose phrasing.

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

All DPX — Institutional Cross-Border Settlement tools Inspect

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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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