Backtesting Arena

Is this difference real — or smaller than the measurement noise?

arena_is_distinguishable

Read-only

Do these two backtest figures actually differ? Check before ranking them. Pass the two values as a and b (same metric, same basis) plus axes — which arbitrary choices went into them — and the tool returns whether their gap clears the MEASURED noise floor of those choices, along with the floor itself, the dominant axis, and the probe + date it was measured on. axes accepts: grid_phase (how a multi-day candle grid is aligned to the Unix epoch; exists only on 2d/3d), parameter_choice (neighbouring parameter settings — by far the largest axis), window_edges (shifting the start date), pair_selection (which pairs made it into the universe). Pass ALL axes that genuinely varied; the floor is their maximum, not their sum. Optionally set interval to the candle interval so the floor can be sharpened where an axis was measured per interval — passing grid_phase together with a non-multi-day interval is a hard error, because that axis does not exist there. label_a and label_b are optional display names for the two values and are echoed back inside the explanation, so a multi-way comparison stays readable. metric selects the figure being compared: cagr (default, gross CAGR in percent), max_drawdown (bar-wise max drawdown in percent) or coin_vs_hold (percent more or fewer coins than buying and holding from the start). Each has its own measured floors; grid_phase is measured for cagr only and errors on the others. Read-only, no market data touched. [Free tier]

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "a": "<a>",
      "b": "<b>",
      "axes": "<axes>"
    },
    "name": "arena_is_distinguishable"
  }
}

Input Schema

Name	Required	Description
`a`	Yes	First value in percent of the chosen metric (e.g. 33.1 for +33.1% CAGR).
`b`	Yes	Second value, same metric, same unit and same basis as a.
`axes`	Yes	Which arbitrary choices differ between a and b. Pass every one that genuinely varied — omitting an axis makes the answer look more certain than it is.
`metric`	No	Which figure a and b are: cagr (default), max_drawdown or coin_vs_hold. Floors differ by metric.
`label_a`	No	Optional name for a, echoed in the explanation.
`label_b`	No	Optional name for b, echoed in the explanation.
`interval`	No	Candle interval, if known (e.g. '1d', '2d', '3d', '1w'). Sharpens the floor where an axis was measured per interval. Passing grid_phase with a non-multi-day interval is an error, not a rounding detail — that axis does not exist there.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 29, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```

Changed3 schema fields changedOct 5, 2026

changedInput schema / properties / a / description

Previous value: -"First value — gross CAGR in percent (e.g. 33.1 for +33.1%)."New value: +"First value in percent of the chosen metric (e.g. 33.1 for +33.1% CAGR)."

changedInput schema / properties / b / description

Previous value: -"Second value, same unit and same basis as a."New value: +"Second value, same metric, same unit and same basis as a."

addedInput schema / properties / metric

Added value: +{
+  "description": "Which figure a and b are: cagr (default), max_drawdown or coin_vs_hold. Floors differ by metric.",
+  "enum": [
+    "cagr",
+    "max_drawdown",
+    "coin_vs_hold"
+  ],
+  "type": "string"
+}

Changed3 schema fields changedAug 25, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

removedInput schema / properties / context

Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}

changedInput schema / required

Previous value: -[
-  "a",
-  "b",
-  "axes",
-  "context"
-]New value: +[
+  "a",
+  "b",
+  "axes"
+]

AddedAug 6, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond readOnlyHint it discloses the return payload (floor, dominant axis, probe and date measured on), the aggregation rule ('the floor is their maximum, not their sum'), and two hard-error conditions (grid_phase with a non-multi-day interval; grid_phase with non-cagr metrics). Annotations already cover safety, and the description still adds substantial behavioral 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?

Front-loaded with the decision question, then parameters in priority order with no filler. It is dense but occasionally repetitive — the grid_phase/interval error is stated in both the axes paragraph and the interval paragraph, and duplicated again in the schema.

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?

With no output schema, the description carries the full burden and does so: it enumerates what is returned, the metric definitions (cagr gross percent, bar-wise max drawdown, coin_vs_hold), and the failure modes. Nothing needed to invoke or interpret the call is missing.

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 coverage is 100%, so the baseline is 3, but the description adds real semantics the schema lacks: what each axis physically represents, that parameter_choice is 'by far the largest', that grid_phase exists only on 2d/3d, how `interval` sharpens the floor, and that labels are echoed into the explanation. It also warns that omitting an axis understates uncertainty.

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 opening states a specific decision verb and resource — decide whether two backtest figures differ beyond the measured noise floor — which is functionally unlike any sibling (the others run, list, or fetch backtests). The parenthetical 'Check before ranking them' further pins the role so an agent cannot confuse it with arena_compare_strategies.

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?

'Check before ranking them' gives a clear use condition and the section on `metric`/`axes` implies the preconditions (same metric, same basis). It never names an alternative tool or a when-not-to-use case, so it stops short of a 5.

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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