Current SPY-realized-volatility regime classification (low/mid/high) and recent regime history for context.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
| Name | Required | Description | Default |
|---|---|---|---|
| today | No | ||
| history | No | ||
| distribution | No | low/mid/high → count in the window | |
| history_days | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about the output (current regime and history) but no additional behavioral traits such as data source or computation method, so it adds only minimal value beyond 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?
The description is a single sentence with no filler, front-loading the primary output ('current SPY-realized-volatility regime classification') and adding the history note.
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-only tool with an output schema and strong annotations, the description is mostly adequate, but it lacks explicit parameter documentation and does not differentiate from sibling tools. The absence of any reference to the 'days' parameter in the description leaves a small but real gap.
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 schema contains a single 'days' parameter with no description (0% coverage). The description mentions 'recent regime history' which hints that 'days' controls the history length, but does not explicitly state its meaning, use, or constraints, leaving the agent to infer the link.
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?
Clearly identifies the tool's function: producing a SPY realized-volatility regime classification (low/mid/high) plus recent history. This differentiates it from sibling tools like get_market_context or get_vx_term_structure by focusing specifically on SPY volatility regimes.
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 gives a clear sense of when to use the tool (when you need SPY volatility regime and recent history), but it does not mention alternatives or exclusions, so it falls short of explicitly guiding selection among siblings.
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 resources (features, embeddings, labels, market context, risk clusters), but minor overlap exists: get_market_context includes a regime reading that get_market_regime also provides, and get_report_card bundles features that get_features offers separately. Descriptions are clear enough to resolve these overlaps.
The predominant pattern is get_<noun> (get_features, get_labels, get_manifest, etc.), with two exceptions: find_similar (find_) and list_futures (list_). This is a small deviation but still follows a predictable verb-noun structure for retrieval, search, and enumeration actions.
14 tools is well within the ideal range for a quantitative data server. Each tool serves a distinct purpose, from basic data retrieval (features, labels) to advanced analytics (similarity, risk clusters) and user management (alerts, usage). No tool feels redundant or missing.
The toolset covers the core data access and analytics needs for factor-based market analysis: retrieval, search, market context, and backtesting labels. Minor gaps include no generic ticker search or list (beyond futures), and no direct way to browse available factors beyond documentation, but these can be worked around via get_top and get_manifest.