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Backtest360

backtest360-mcp

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by Backtest360

compare_backtests

Run several trading strategies on identical data and view side-by-side comparisons, with support for benchmarks and adjustable result detail.

Instructions

Run several strategies on the same data and compare side by side.

One quota-counted call, but compute scales with the number of strategies. The engine enforces a wall-clock budget for the whole comparison; when it runs out mid-way the response carries "truncated": true and the remaining strategies are missing — report that to the user rather than re-running blindly.

Args: data_source: Shared data source (same shape as run_backtest). strategies: List of {"label": str, "strategy": {...}, "execution": {...}?} entries. include_benchmark: Add a buy-and-hold benchmark to the comparison. response_detail: Shaping level applied to each strategy's result. trades_limit: Max trades per strategy when detail is 'full'.

Returns: {"strategies": [{"label", "result"}, ...], "equity_curves": {...}}, each result shaped at the requested detail. Two truncation flags are distinct and may both appear: the engine's "truncated" (wall-clock budget exhausted mid-comparison — strategies are missing) and the MCP size-cap marker "truncated_by_mcp". A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
strategiesYes
data_sourceYes
trades_limitNo
response_detailNosummary
include_benchmarkNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: quota counting, compute scaling, wall-clock budget, truncation mechanisms (engine vs MCP), error response formats, and return structure. This is comprehensive.

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 well-structured with clear purpose, Args, and Returns sections. Every sentence adds necessary information without redundancy. Front-loaded with 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 the complexity (5 parameters, nested objects, output schema), the description covers inputs, outputs, error cases, truncation, and edge conditions. It leaves no critical gap for an AI agent.

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?

Despite 0% schema coverage, the description includes a detailed Args section explaining each parameter's meaning and expected structure (e.g., strategies as list of objects, response_detail enum). This adds significant value beyond the bare 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 the action: 'Run several strategies on the same data and compare side by side'. It uses specific verb 'run' and resource 'strategies', and the comparison aspect differentiates it from sibling tools like run_backtest.

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 provides context on quota costing and scaling, and advises against blind re-runs on truncation. However, it does not explicitly specify when to use this tool versus alternatives (e.g., run_backtest) or when not to use it, leaving some ambiguity.

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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