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Backtest360

by com.backtest360

get_catalog

Fetch one engine reference catalog.

    Catalogs (cheap, cacheable per session):
    - 'operators' — comparison operators for condition expressions
    - 'execution-modes' — entry/exit anchors and fill algorithms, with the
      validity matrix by market type
    - 'stop-types' — stop-loss types, re-entry modes, and their parameters
    - 'sizing-methods' — position-sizing methods and their parameters
    - 'bar-frequencies' — supported bar frequencies and the signal x
      execution validity matrix (which combinations are allowed)
    - 'sections' — the full metric catalog: every statistic's stable id,
      display label, section, and description
    - 'sampling-modes' — Monte-Carlo resampling modes, each with its
      status and parameters

    Fetch the relevant catalog BEFORE building a strategy or config; build
    only from values it lists — never guess parameter names or frequencies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catalogYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior4/5

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

Describes catalogs as 'cheap, cacheable per session', indicating they are inexpensive and safe to call repeatedly. No annotations exist, so the description carries the burden; it could mention response format or idempotency but provides useful context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a clear structure with a bold opening sentence and bullet points for each catalog. While it is somewhat lengthy, every sentence and bullet adds value, justifying the length.

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 simple tool with one parameter and an output schema, the description covers all enum values, provides usage context, and includes behavioral notes. No gaps are apparent given the tool's complexity.

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 single parameter 'catalog' is an enum with no schema descriptions (0% coverage). The description compensates fully by listing all seven enum values with detailed explanations of each, far exceeding what the schema provides.

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 'Fetch one engine reference catalog' and lists all seven specific catalog types, distinguishing it from sibling tools like compare_backtests or compute_stats which perform different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises to 'Fetch the relevant catalog BEFORE building a strategy or config' and instructs to 'build only from values it lists — never guess parameter names or frequencies', providing clear when-to-use and how-to-use guidance.

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
Disambiguation5/5

Every tool targets a distinct operation or resource: backtesting, comparison, macro data, reference catalogs, etc. Even similar tools like run_backtest and compare_backtests are clearly differentiated by purpose and inputs.

Naming Consistency4/5

Overall consistent verb_noun pattern in snake_case, with a few exceptions like engine_info (noun_noun) and export_backtest (verb_noun but less common verb). The pattern is predictable and aids agent selection.

Tool Count4/5

20 tools is slightly above the ideal range but justified by the breadth of the platform (backtesting, data retrieval, reference, export). Each tool serves a clear purpose without redundancy.

Completeness4/5

Covers the full backtesting lifecycle: strategy validation, data sourcing, backtesting, comparison, export, and reference lookups. Minor gaps exist (e.g., no explicit strategy persistence), but the core workflow is complete.

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