Cancel an intelligence subscription by ID. Stops future webhook alerts for that subscription. The alert log is retained for audit purposes.
| Name | Required | Description | Default |
|---|---|---|---|
| subscriptionId | Yes | UUID returned by intelligence.subscribe. |
| Name | Required | Description | Default |
|---|---|---|---|
| deleted | No | ||
| subscriptionId | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states 'Stops future webhook alerts', 'The alert log is retained for audit purposes', which provides critical behavioral context beyond the annotations. The annotations already declare destructiveHint=true and idempotentHint=true, but the description explains exactly what is destroyed (future alerts) and what is preserved (log), adding transparency and managing expectations.
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 concise, front-loaded sentences. Each sentence delivers distinct and essential information: what it does, what it stops, and what it retains. No wasted words.
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 low complexity (1 param, clear schema, good annotations), the description is adequately complete. An output schema exists, so return values need no explanation. A slightly higher score would require mentioning prerequisites (e.g., valid subscription ID) or error conditions.
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 coverage is 100% with only one parameter 'subscriptionId' described as 'UUID returned by intelligence.subscribe.' The description doesn't repeat the schema, but it implies the parameter is a unique identifier. A higher score would require adding context like 'Use the subscriptionId from the subscribe response' directly in the description, but the schema already provides a clear description.
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 specific verb ('Cancel') and resource ('intelligence subscription by ID'), immediately and precisely conveying what the tool does. It also distinguishes clearly from the sibling 'intelligence.subscription.get' (which retrieves) and 'intelligence.subscribe' (which creates), making the purpose unambiguous.
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 states 'Stops future webhook alerts for that subscription', clarifying the effect of use. It does not explicitly specify when NOT to use it (e.g., if alerts are still needed) or name an alternative tool for modifying subscriptions, but given the one-purpose tool design and sibling list, the implication is clear.
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.