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GammaRips Options Intelligence

Methodology Playbook

get_playbook
Read-onlyIdempotent

Retrieve methodology playbooks, field definitions, or the data contract to understand signal fields and leakage-safe selection inputs.

Instructions

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnly/idempotent annotations: it is versioned server-side and should be re-fetched rather than cached long-term; field lookup is deterministic with no LLM; `field` overrides `name`; and the data contract marks only `feature` columns as safe selection inputs. This significantly helps an agent use the tool safely and correctly.

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 detailed but efficiently organized with a short opening, a bulleted arg-driven breakdown, and a compact parameter list. Every sentence adds useful information, and the structure makes the branching behavior easy to parse. The length is justified by the tool's multi-mode behavior.

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?

With no output schema present, the description must explain return values, and it does: field mode returns a definition plus an `available_fields` list, schema mode returns a data contract with leakage classifications, named playbooks return markdown, and no-args returns a catalog. The precedence rule and safe-feature caveat complete the picture for correct invocation.

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 has 0% description coverage, so the description carries the full burden of explaining both parameters. It fully documents valid values for `name` (including special values "schema" and "data-contract"), explains `field` with an example, and explicitly states precedence: `field` overrides `name`.

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 resource (methodology playbook/reference) and enumerates exactly what the tool returns in each mode: field definitions, named playbooks, the data contract, or a catalog. It clearly differentiates itself from sibling tools like get_signal or get_pool by scoping its content to methodology and reference rather than live market data.

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 argument-driven usage rules: if `field` is given, look up a field definition; if `name` is given, return a playbook or special schema page; if neither, return the catalog. It does not explicitly discuss when to prefer a sibling tool, but the mode routing is clear enough that an agent can decide when this tool is appropriate.

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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