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Backtest360

by com.backtest360

get_price_history

OHLCV price history for a symbol over a date range.

    Requires a paid plan (managed market data). ``start`` is required
    (``YYYY-MM-DD``); ``end`` defaults to today. Returns a summary (symbol,
    resolved date range, total bar count, price range, gap flags),
    market-hours detection, and the OHLCV arrays. A long history is
    downsampled by the MCP server to a bounded number of points — first and
    last bar always kept, every column thinned on the same dates — with
    ``downsampled_from_bars`` and ``points_returned`` recorded on the
    ``ohlcv`` block; the untouched ``summary.total_bars`` still reports the
    true bar count. The window is bounded by the plan's per-request bar cap
    — call get_data_range first to size a request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startYes
symbolYes
frequencyNodaily

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It extensively discloses behavioral traits: downsampling behavior (first/last bar always kept, thinning on same dates), return structure (summary, market-hours, OHLCV arrays), plan cap, and bounded window. This is comprehensive for a price history 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 a single dense paragraph that covers all essential aspects without waste. While it could be more structured (e.g., bullet points), it remains concise and front-loaded with the core purpose.

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 an output schema exists, the description need not detail return values exhaustively. It still provides sufficient context: mentions summary, market-hours, OHLCV arrays, downsampling info, and plan cap. This is complete for an agent to understand the tool's behavior and output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning for start (YYYY-MM-DD), end (defaults to today), and symbol (implicit). However, the frequency parameter is not mentioned despite having a default of 'daily'. This is a minor gap but overall helpful.

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 it provides 'OHLCV price history for a symbol over a date range', using a specific verb-resource combination. It distinguishes from siblings like get_quote (single snapshot) and get_data_range (sizing helper) by mentioning that the window is bounded and to 'call get_data_range first to size a request'.

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 when to use the tool (for OHLCV history), provides format for start (YYYY-MM-DD) and default for end (today), and mentions the prerequisite of a paid plan and the need to call get_data_range first. It does not explicitly state when not to use, but the context is clear enough.

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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