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GammaRips Options Intelligence

by io.github.DevDizzle

Methodology Playbook

get_playbook
Read-onlyIdempotent
Methodology + reference, versioned server-side (re-fetch rather than
caching long-term). Arg-driven:

  * `field` given — the plain-English DEFINITION + role of a signal field
    (deterministic lookup, no LLM). e.g. field="mom_60". The response's
    `available_fields` lists every documented field.
  * `name` given — a methodology playbook (markdown) by name, OR two
    special reference pages:
      - name="schema" (or "data-contract") -> the machine-readable
        substrate DATA CONTRACT: every outcome/label column with its
        leakage classification (feature|label|opportunity|
        regime_telemetry|identity) and as-of boundary. Only `feature`
        columns are safe as selection inputs.
      - any other name -> the playbook markdown (start-here,
        daily-workflow, run-your-own-tournament, exit-lab,
        leakage-and-data-contract, changelog).
  * neither — the CATALOG of published playbooks (name/title/summary),
    plus a pointer to the field dict (`field=`) and schema page.

Args:
    name: playbook name, or "schema"/"data-contract" for the data contract.
    field: a signal field name to explain (overrides `name`).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
fieldNo

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive; the description adds substantial behavioral context: versioned server-side content, a recommendation to re-fetch rather than cache, deterministic no-LLM field lookups, override semantics between `field` and `name`, and the leakage-classification safety rule that only `feature` columns are safe as selection inputs. This goes well beyond what annotations 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 front-loaded with the tool's essence and uses a compact bullet structure that maps cleanly to the branch logic. Every sentence contributes either a mode, an example, or a constraint; there is no 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?

Despite having no output schema, the description explains what each invocation returns: field definitions with an `available_fields` list, markdown playbooks, a machine-readable data contract with leakage classifications, or a catalog with a pointer to the field dictionary. Valid name values are enumerated, making the tool fully callable without external assumptions.

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. It thoroughly explains `name` with valid special values and concrete playbook names, and `field` with an example and the note that it overrides `name`. This fully compensates for the bare schema.

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 purpose: a versioned server-side methodology/reference tool that returns field definitions, playbook markdown, the data contract, or a catalog depending on arguments. It clearly differentiates this from data-access siblings like get_signal or get_pool by framing it as the methodological/dictionary resource.

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 explicit arg-driven usage rules: use `field` for definitions, `name` for playbooks or the data contract, and neither for the catalog. It also enumerates valid playbook names and special values. It does not explicitly contrast with sibling tools, but the usage context is clear enough that an agent can select this tool confidently.

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.

Resources