Get the aggregated payment graph for a multi-agent session. Returns total USD moved, transaction count, and a chronological list of all payments made during the session. Use for cost accounting, audit, or to show a human what an agent run spent.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | Session or task ID — the same ID used in receipt.create calls |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description confirms it returns data but adds no additional behavioral context (e.g., pagination, rate limits, error handling). It meets the baseline but does not exceed it.
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?
Three sentences, front-loaded with the primary action and return values, followed by usage guidance. Every sentence is necessary and no words are wasted.
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 simple read tool with one parameter and no output schema, the description fully covers what the tool does, what it returns, and when to use it. No critical gaps remain.
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 sole parameter session_id is already described in the schema. The description adds value by clarifying that the ID is the same one used in receipt.create calls, providing helpful cross-reference context beyond the schema.
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 clearly states the tool retrieves aggregated payment data for a multi-agent session, specifying the exact return fields (total USD moved, transaction count, chronological list). It distinguishes itself from siblings like receipt.create by focusing on session-level aggregation.
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?
Explicitly lists three use cases: cost accounting, audit, and showing human what an agent run spent. While it does not mention when not to use it or alternatives, the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.