FactorWeave

get_market_context

Read-onlyIdempotent

Whole-universe derived market analytics: factor dispersion (10-90 percentile spreads of momentum / composite / RSI), breadth (% of the universe positive / overbought / oversold), and the current SPY-volatility regime with empirical next-day transition odds. FREE gets today's reading; HOBBY+ can also get the 252-day history (pass history=true).

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
`date`	No
`regime`	No
`breadth`	No	Per-factor % positive / overbought / oversold.
`history`	No	252-day history (HOBBY+ only). Each row carries the per-day cross-asset z-scores + vx_term_structure for charting.
`dispersion`	No	Per-factor 10–90 percentile spread.
`cross_asset`	No	DXY / VIX / VVIX / VIX9D / TNX / XAU / VX-continuous closes + 20d log-return z-scores, plus vx_term_structure = (VX_continuous − VIX_cash) / VIX_cash.
`regime_transition_odds`	No	Empirical next-day regime-transition probabilities.

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context beyond that: the FREE vs HOBBY+ tier restriction, the history boolean parameter behavior, and the 'empirical next-day transition odds' detail. This gives a richer picture of what the tool returns and under what conditions, without contradicting annotations.

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 compact and well-organized: one sentence catalogs the analytics, and a second sentence explains tiered access. No wasted words; every phrase earns its place. The structure is front-loaded with the defining purpose before procedural details.

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?

The tool has a single simple parameter, an output schema, and no nested objects. The description covers what data is returned, the tier constraints, and the history option. For a read-only analytics tool, this is sufficiently complete.

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% with the history parameter already described. The description adds the contrast between FREE (today's reading) and HOBBY+ (history) and explicitly mentions 'pass history=true', which clarifies the parameter's effect in context. This goes slightly beyond the schema by tying the parameter to the tier system.

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 states the tool provides whole-universe derived market analytics, enumerating the specific metrics (factor dispersion, breadth, SPY-volatility regime). This differentiates it from siblings like get_market_regime and get_vx_term_structure, which focus on other aspects. The scope and content are explicit.

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 what the tool does and includes tier-based guidance: FREE gets today's reading, HOBBY+ can also get history. It doesn't explicitly compare to alternatives, but the unique metrics make the intended use clear. No exclusions or alternative tools are named, so it misses the top score.

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.

Resources

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