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Backtest360

by com.backtest360

get_macro_series

Observations for one macroeconomic series over an optional date range.

    Free — no special plan. ``series`` is an ``id`` from list_macro_series
    (e.g. treasury_10y, cpi, unemployment_rate); arbitrary external ids are
    not accepted. ``start``/``end`` are ``YYYY-MM-DD``, inclusive, both
    optional (full history when omitted). Returns the value series at its
    native reporting frequency, with the series descriptor and an ``as_of``
    date. A long history is downsampled by the MCP server to a bounded
    number of points (first and last kept), marked with
    ``downsampled_from_bars`` and ``points_returned`` on the
    ``observations`` block.

    Note: values are the latest revised figures stamped by reference period,
    not point-in-time as-first-reported data — do not treat them as the
    values that were known at a past date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startNo
seriesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries full burden. It discloses that the data is free, returns observations at native frequency, includes series descriptor and as_of date, and downsamples long histories with markers. Crucially, it warns that values are latest revised figures, not point-in-time, which is a key behavioral trait for a data retrieval tool.

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 is somewhat long but front-loaded with the core purpose. Each sentence contributes value: purpose, free access, parameter constraints, downsampling behavior, and a note about revised figures. While a bit verbose, it is well-structured without redundant content.

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 that an output schema exists (stated in context), the description appropriately covers the tool's purpose, parameters, behavior (downsampling, frequency), and data characteristics (revised figures). It provides sufficient context for an agent to use the tool correctly without needing additional documentation.

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 must add meaning. It explains that series is an id from list_macro_series with examples, and describes start/end as dates in YYYY-MM-DD format, optional, inclusive. This provides essential context beyond the schema's type and default fields.

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 the verb 'get' and the resource 'macro series' with constraints: one series over an optional date range. It distinguishes from sibling tools like list_macro_series (which lists series) and other data retrieval tools by specifying the unique purpose of fetching observations for a single macroeconomic 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 explains that the tool is free and that the series parameter must be an id from list_macro_series, with examples. It notes that start/end are optional and the date format. However, it does not explicitly state when to use this tool versus alternatives like get_price_history for stock data, though the context implies the differentiation.

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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