rank_vaultsReturn the current evidence-gated VaultVision ranking snapshot. Ranking is a first-party model and must not be presented as guaranteed future performance.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ranking_type | No | risk-adjusted |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the 'evidence-gated' nature and 'first-party model' provenance qualifier, which usefully frames the ranking's reliability limits beyond what annotations provide. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two front-loaded sentences with zero filler; the core action leads and the caveat trails. Every clause earns its place.
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?
An output schema exists to document return values, and no parameters are required. However, the choice among ranking_type values (risk-adjusted vs verified vs estimated) is left unexplained, which is a genuine usability gap for an agent needing to select a ranking methodology.
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%, so the description carries the full burden for explaining parameters, yet it explains neither `limit` nor `ranking_type`. The enum values are semi-self-describing but the description never maps rank types to use cases. With zero schema coverage, the description must compensate and fails to.
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?
States a specific verb ('Return') and a precise resource ('VaultVision ranking snapshot') qualified by 'evidence-gated' and 'current'. The caveat about not presenting as guaranteed future performance adds fidelity. This is clearly distinct from siblings like get_vault (single vault) and compare_vaults (side-by-side comparison).
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?
No explicit when-to-use or when-not-to-use guidance, and no sibling alternative is named. The 'must not be presented as guaranteed future performance' caveat offers mild usage framing, but there is no selection logic distinguishing this from search_vaults 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.