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Backtest360

by com.backtest360

list_macro_series

List the available macroeconomic series (the catalog).

    Free — no special plan. Returns the set of macro series you can fetch
    with get_macro_series, each with its stable ``id`` (the value
    get_macro_series takes), title, category, native reporting frequency,
    and units, plus the list of categories. Optionally filter to one
    ``category`` (e.g. rates, yield_curve, inflation, employment, recession,
    growth). Call this first to find the ``id`` for the series you want.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It states the tool is free, returns a catalog with specific fields, and allows optional filtering. No side effects or destructive behavior are present, and the read-only nature is implied.

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 concise, front-loaded with the main purpose, and uses clear phrasing without extraneous words. It effectively organizes information with a brief introductory sentence followed by details.

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 listing tool with one optional parameter, the description covers the purpose, return fields, usage guidance, and parameter details. The presence of an output schema (not shown) may complement, but the description stands alone as complete.

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 schema description coverage is 0%, meaning the schema has no parameter descriptions. The description compensates by explaining the sole parameter 'category' with concrete examples (rates, yield_curve, inflation, etc.) and its purpose (filtering), adding significant meaning beyond the 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 clearly states that the tool lists available macroeconomic series (the catalog), specifies what it returns (id, title, category, frequency, units), and distinguishes it from get_macro_series which fetches a specific series.

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 advises to 'Call this first to find the id for the series you want,' providing clear guidance on when to use this tool before get_macro_series. It also mentions optional category filtering but does not compare with all sibling tools.

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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