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Backtesting Arena

by io.github.Schoasch

Get Volatility Insights

arena_get_volatility_insights

Does this strategy work better in calm or wild markets? Breaks realized strategy performance down by VOLATILITY PHASE (low / normal / high) per asset and timeframe, so you can see whether an edge only exists in one volatility regime. Answers "when does this work", not "does this work" — for the overall verdict use arena_get_strategy_insights, for the macro-regime cut arena_get_strategy_performance_by_regime, and for the raw volatility time series arena_get_volatility_history. Cells below min_trades are suppressed rather than shown as noise. [API Pro tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_typeNoFilter by asset class, e.g. 'crypto'. Omit for all.
min_tradesNoMinimum trades a cell needs to be reported. Default 20 — lowering it buys coverage with noise.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • 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"
-}
    • removedInput schema / required
      Removed value: -[
-  "context"
-]
  2. Changed2 schema fields changed
    • addedInput schema / properties / asset_type / description
      Added value: +"Filter by asset class, e.g. 'crypto'. Omit for all."
    • changedInput schema / properties / min_trades / description
      Previous value: -"Minimum trades per cell. Default 20."New value: +"Minimum trades a cell needs to be reported. Default 20 — lowering it buys coverage with noise."
  3. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto"
-]New value: +[
+  "crypto",
+  "tokenized_equity",
+  "tokenized_etf",
+  "commodities"
+]
  4. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto",
-  "stock",
-  "etf",
-  "commodities",
-  "forex"
-]New value: +[
+  "crypto"
+]
  5. First observed

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that cells below min_trades are suppressed rather than shown as noise, and mentions the API Pro tier requirement. While it doesn't explicitly state that the operation is read-only, the tool name and 'get' prefix imply it, and the description gives meaningful behavioral context without contradicting anything.

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 yet information-dense. It opens with an engaging question, states the core functionality, differentiates from three siblings, and discloses suppression behavior and tier requirement—all in three sentences. Every sentence earns its place, and the key purpose is front-loaded.

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 reporting tool with two optional parameters and no output schema, the description is complete. It explains the volatility phase concept, defines what the output reveals, and covers edge cases (suppression). It also names alternatives, so an agent can route correctly. Nothing essential is missing for correct invocation.

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

Parameters3/5

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

Schema description coverage is 100%: both parameters have clear schema descriptions (asset_type lists enum options; min_trades explains default and trade-off). The tool description reinforces min_trades meaning by mentioning suppression, but adds no additional semantic value beyond what the schema already documents. Baseline 3 is appropriate given full schema coverage.

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 tool breaks down strategy performance by volatility phase (low/normal/high) per asset and timeframe, answering 'when does this work' rather than 'does this work'. It explicitly differentiates itself from three sibling tools by naming them and their distinct purposes, leaving no ambiguity about what this specific tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives: use it for the volatility-phase breakdown, use arena_get_strategy_insights for the overall verdict, arena_get_strategy_performance_by_regime for macro-regime cuts, and arena_get_volatility_history for raw volatility series. It also explains the min_trades suppression behavior, which is a usage consideration.

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

A3.6/5.0
Disambiguation2/5

Many tools cover overlapping market indicators (e.g., cycle state, pulse, bullmarket ampel, volatility phases) and several share similar get_*_history patterns, which could cause an agent to select the wrong one. However, each tool has detailed descriptions with explicit references to related tools to reduce ambiguity.

Naming Consistency3/5

Tool names generally follow a verb_noun pattern (arena_get_*, arena_list_*, arena_run_*, arena_subscribe_*), but there are inconsistencies such as 'validate_strategy' (no arena_ prefix), 'arena_status' (not a clear verb_noun), and variations like 'arena_run_grid_backtest' vs 'arena_run_backtest'.

Tool Count2/5

With 84 tools, the server is heavily over-scoped for a typical MCP server, likely causing navigation and selection overhead. While the domain (crypto backtesting and analytics) is broad, 84 tools exceed reasonable coherence and impose a high cognitive load.

Completeness4/5

The tool surface covers a wide range of analytics (market indicators, backtesting, validation, alerts, subscriptions, reports) with few obvious dead ends. Minor gaps exist like lack of direct portfolio management or strategy editing, but core workflows are well covered.

Resources