DPX — Institutional Cross-Border Settlement

forecast.calendar

Read-onlyIdempotent

Seasonal climate event calendar with commodity impact. Returns 12 critical annual windows (hurricane season, corn pollination, Brazil frost risk, Black Sea harvest, ENSO influence periods, etc.) sorted by urgency — active windows first, then by months until next occurrence. Each entry includes affected commodities, severity, and the agronomic basis.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`calendar`	No	Seasonal events sorted by urgency — active events first
`currentMonth`	No	Current UTC month (1–12) for reference

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so the non-mutating, safe nature is clear. The description adds significant context: returns 12 sorted events with commodity impact details, severity, and agronomic basis, which enriches the behavioral understanding beyond just 'read-only.' No contradiction with 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 two sentences with zero waste. The first sentence immediately establishes the core purpose (seasonal climate calendar with commodity impact). The second efficiently details the 12-window scope, sorting logic, and output fields. Every sentence is necessary and front-loaded.

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 zero parameters, an output schema existing, and clear annotations, the description needs only to clarify what the tool returns and how it's organized. It fully covers the purpose, sorting logic, content (affected commodities, severity, agronomic basis), and scope (12 annual windows). No gaps remain for a read-only, no-param tool.

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

Parameters4/5

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

The input schema has zero parameters and 100% coverage, so there is nothing to describe beyond what the schema provides. The description adds value by clarifying what the output contains (12 windows, sorted, with commodities/severity/agronomic basis) but does not need to explain parameters.

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?

The description clearly states it returns a 'seasonal climate event calendar with commodity impact,' specifying the resource (calendar) and the verb (returns). It distinguishes itself from siblings like forecast.commodity_outlook by focusing on a pre-defined set of 12 annual windows, not broad commodity predictions.

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?

The description implies use for understanding seasonal climate risks and their commodity impact, but lacks explicit guidance on when to use this versus other forecast tools (e.g., forecast.scenario for custom scenarios, forecast.commodity_outlook for commodity-specific trends). No 'when-not-to-use' or alternative strategies are mentioned.

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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