FactorWeave

get_vx_term_structure

Read-onlyIdempotent

VIX futures contango/backwardation read: (VX_continuous − VIX_cash) / VIX_cash. Negative = backwardation (near-term realized vol > expected forward vol — classic stress / vol-panic signal). Positive = contango (term structure normal, calm regime). Pass history=true (HOBBY+) for the 252-day series for charting. Single-number version of the vol-curve regime that traders watch every day.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`history`	No	Include the 252-day history (HOBBY tier or higher).

Output Schema

TableJSON Schema

Name	Required	Description
`as_of`	No
`regime`	Yes
`history`	No	252-day history (HOBBY+ only).
`vx_close`	No
`vix_close`	No
`vix9d_close`	No
`vx_term_structure`	Yes	(VX_continuous − VIX_cash) / VIX_cash. Negative = backwardation; positive = contango.

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context: the formula, the sign interpretation, and the tier requirement (HOBBY+) for history. It also clarifies that the default returns a single number, aligning with the histogram parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: it opens with the core formula, then explains the two states (negative/positive) with their market implications, and ends with the optional parameter. Every sentence contributes value, and the formatting makes it easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description does not need to detail return types. It sufficiently covers what the tool computes, how to interpret the result, and the parameter behavior. The tier limitation is also disclosed, making the context complete for agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single 'history' parameter, which already includes its description. The description adds extra usefulness by specifying that history is for charting and uses 'HOBBY+' shorthand, which complements the schema's 'HOBBY tier or higher' phrasing. This gives slightly more contextual meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool's purpose as providing a VIX futures contango/backwardation read, with the exact formula and interpretation. It distinguishes itself from siblings like get_market_regime or get_market_context by specifically targeting the VIX term structure.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on when the metric is meaningful (positive=calm, negative=stress/vol-panic) and how to request history for charting. It does not explicitly mention alternative tools or when not to use it, but the context is strong enough for an agent to decide appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.

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