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GammaRips Options Intelligence

by io.github.DevDizzle

Market Regime Context

get_regime_context
Read-onlyIdempotent
Point-in-time market-regime context for a scan date: VIX close, VIX3M,
SPY trend state, and the 5-day VIX delta — all as-of <= scan_date (the
selection point, leakage-safe), plus the engine's regime safety rail
evaluated on those values.

The rail: the engine fail-closes (no trade) when spot VIX > VIX3M
(backwardation — the market pricing imminent volatility is an adverse
regime for short-dated directional longs).

Served from the labeled substrate, which lags the live pool by ~1-2
trading days. Values are constant per scan_date.

Args:
    scan_date: YYYY-MM-DD. Defaults to the latest scan date carrying
        regime features.

Returns:
    {scan_date, vix_at_scan, vix3m_at_enrich, spy_trend_at_scan,
     vix_5d_delta_at_scan, regime_rail_pass, rail_definition}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scan_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses important behaviors: values are constant per scan_date, data lags the live pool by ~1-2 trading days, and the engine fail-closes when spot VIX exceeds VIX3M. This adds real decision-relevant context that annotations alone do not provide.

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 dense but well organized: a front-loaded summary, a concise explanation of the safety rail, a data-lag caveat, then clear Args and Returns sections. Every sentence contributes useful information without redundancy or filler.

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?

For a single-optional-parameter read-only lookup, the description is complete: it defines the input, defaults, output fields, timing semantics, and the fail-closed rule. An agent has everything needed to call it correctly and interpret the result.

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

Parameters5/5

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

The input schema provides only a nullable string with no description, but the description fully documents scan_date: its format (YYYY-MM-DD), its role as the selection point, and its default behavior (latest scan date carrying regime features). This fully compensates for the 0% schema description coverage.

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 states a specific verb and resource: it returns point-in-time market-regime context for a scan date, enumerating the exact fields (VIX close, VIX3M, SPY trend, 5-day VIX delta) and the regime safety rail. This makes it clearly distinguishable from sibling tools like get_liquidity or get_signal.

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 for when the tool is appropriate: it is a leakage-safe, as-of-scan_date regime lookup that defaults to the latest available scan date, and it is tied to the engine's fail-closed regime check. It does not explicitly name alternatives or state when not to use it, but the intended use is well implied.

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

A4.7/5.0
Disambiguation5/5

Each tool owns a clearly distinct surface: reports, liquidity, market calendar, methodology, pool data, regime context, per-ticker signals, outcome research, and raw price replay. Even the closest neighbors (get_pool, get_signal, get_daily_report) are separated by pool-wide versus single-ticker versus report granularity, and the view parameters remove ambiguity.

Naming Consistency4/5

Seven tools follow a consistent get_<noun> pattern, while query_outcomes and replay_contract deviate with a different verb style. All names are snake_case and predictable, so this is a minor inconsistency rather than chaotic naming.

Tool Count5/5

Nine tools is well within the ideal range for an intelligence and research server. Each tool earns its place, covering a distinct part of the workflow without bloat or thinness.

Completeness5/5

The toolset covers the full research workflow: market/reference context, methodology, candidate pool, live liquidity, per-ticker signals, regime context, outcome research, and raw price replay for custom exit rules. There are no obvious dead ends or missing lifecycle steps for the server's stated purpose.

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