Skip to main content
Glama

GammaRips Options Intelligence

by io.github.DevDizzle

Signal Detail

get_signal
Read-onlyIdempotent
Deep dive on a single ticker/contract. Two `view`s:

  * view="detail" (DEFAULT) — the full enriched signal for one ticker:
    thesis, catalyst, the recommended contract, and point-in-time
    features (leakage-safe view). `full=true` includes the long narrative
    (news_summary, flow_intent_reasoning). If the ticker isn't in the
    pool for the date, the error lists the dates on which it does appear.
  * view="earnings" — the doctrine earnings-window check (RM-003): the
    next scheduled earnings date and whether it lands ON OR BEFORE the
    contract expiration (`earnings_in_window`). The engine applies this
    rail only at its own pick time, NOT in the pool, so pool rows CAN
    carry earnings-window names — check every candidate yourself. Pass
    the pool's `recommended_contract` and both ticker and expiration are
    derived. FAIL-CLOSED: unknown date -> earnings_in_window=null, treat
    as in-window.

Args:
    ticker: underlying symbol (required for detail; optional for earnings
        if `contract` is given).
    view: "detail" (default) | "earnings".
    scan_date: detail only — YYYY-MM-DD (default: latest for the ticker).
    full: detail only — include the long narrative fields.
    expiration: earnings only — option expiration YYYY-MM-DD to test.
    contract: earnings only — OCC ticker supplying ticker+expiration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNo
viewNodetail
tickerNo
contractNo
scan_dateNo
expirationNo

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description goes well beyond this by disclosing fail-closed behavior for unknown earnings dates, leakage-safe point-in-time features, the error behavior when a ticker is missing from the pool, and the distinction between engine-applied rails and pool rows. This is rich, useful behavioral context.

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 structured with a clear opening, a bulleted breakdown of the two views, fail-closed warning, and a compact Args section. There is no filler; every sentence adds either a semantic detail, a default, or a behavioral constraint. Despite its length, it is dense and front-loaded with the most important information.

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 tool's two-view complexity and the presence of an output schema (which removes the need to describe return values), the description is remarkably complete. It covers defaults, parameter combinations, error behavior, edge cases (unknown earnings date), and the rationale for using the earnings view. An agent has everything needed to invoke the tool correctly across both views.

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?

Schema description coverage is 0%, so the description carries the full burden of explaining all six parameters. It does this thoroughly: each parameter is named, tied to a view, marked as required/optional, and given a format (e.g., YYYY-MM-DD, OCC ticker). It even clarifies derivations like 'both ticker and expiration are derived' from contract. This fully compensates for the absent schema descriptions.

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 opens with a specific verb and resource: 'Deep dive on a single ticker/contract.' It distinguishes itself from sibling list-oriented tools by emphasizing it covers one ticker, and it clearly enumerates the two available 'view's. This is more than sufficient for an agent to understand what the tool does.

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 clearly indicates the tool is for a single ticker/contract deep dive and provides detailed per-view guidance, including when to pass 'ticker' vs 'contract' and how to interpret the earnings check. It references the pool's 'recommended_contract' and tells the user to check every candidate, which implies the screening/listing sibling (get_pool) is the alternative. However, it never explicitly names an alternative tool or states a when-not-to-use condition, so it falls just short of a 5.

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

All GammaRips Options Intelligence toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources