explain_vault_riskExplain one vault's current VaultVision risk score, component values, drawdown evidence, and data caveats without converting the model into investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| vault | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful context by specifying the output scope and explicitly noting it avoids converting the model into investment advice, which is a useful behavioral boundary.
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?
The description is a single, front-loaded sentence with no filler. Every phrase contributes meaning: the action, the object, the specific outputs, and the behavioral caveat.
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?
With output schema present, the description does not need to detail return values. It covers the tool's purpose, scope, and an important caveat. The main gap is the lack of guidance on what the 'vault' parameter should contain, but the overall context is reasonably complete for a simple read-only tool.
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 description coverage is 0% and the description only says 'one vault,' giving minimal meaning to the 'vault' parameter. It does not compensate for the schema's lack of detail by explaining whether the parameter expects an ID, address, name, or other identifier.
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 names a specific verb ('Explain') and a precise resource ('one vault's current VaultVision risk score, component values, drawdown evidence, and data caveats'). It clearly distinguishes itself from sibling tools like rank_vaults or compare_vaults by focusing on a single vault's risk explanation.
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 implies when to use the tool: when a user wants an explanation of one vault's risk rather than ranking, comparison, or raw data. However, it does not explicitly state alternatives or describe when not to use this tool over siblings like get_vault or compare_vaults.
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 target distinct actions and resources, but search_vaults and rank_vaults both return ordered vault lists, and fetch and get_vault can retrieve similar single-record data. The descriptions provide enough context to disambiguate in most cases.
Tool names generally follow a clear verb_noun snake_case pattern (compare_vaults, get_vault, rank_vaults). Standalone verbs like fetch and search are minor deviations but remain readable and consistent in style.
Nine tools is well-scoped for a research-focused server. Each tool covers a distinct investigation workflow without redundant entries or excessive surface area.
The server covers discovery, retrieval, comparison, ranking, risk explanation, alerts, and HLP-specific metrics. Minor gaps exist, such as no historical/time-series tool, but core research workflows are fully supported.