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Backtesting Arena

by io.github.Schoasch

Get Strategy Recommendations for Current Volatility Phase

arena_get_volatility_recommendations

Which strategies suit the current volatility phase? Top-3 by historical win-rate for that phase on a given pair. Phase comes from the latest snapshot (arena_get_volatility_phases); minimum 20 trades per phase required for inclusion. Answers "which strategies did well in a phase like the current one?" — a historical ranking of what held up in comparable phases. Related: arena_get_volatility_phases (the phase itself), arena_get_edge_reports (filter effects with verdicts), validate_strategy (evidence check on a concrete configuration). [API Pro tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pairYesCrypto asset pair, e.g. "BTCUSDT", "ETHUSDT"
asset_typeNoAsset class of the paircrypto

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"
-}
    • changedInput schema / required
      Previous value: -[
-  "pair",
-  "context"
-]New value: +[
+  "pair"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto"
-]New value: +[
+  "crypto",
+  "tokenized_equity",
+  "tokenized_etf",
+  "commodities"
+]
  3. Changed2 schema fields changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto",
-  "stock",
-  "etf"
-]New value: +[
+  "crypto"
+]
    • changedInput schema / properties / pair / description
      Previous value: -"Asset pair, e.g. \"BTCUSDT\" for crypto, \"AAPL.US\" for stocks"New value: +"Crypto asset pair, e.g. \"BTCUSDT\", \"ETHUSDT\""
  4. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: the output is a top-3 ranking by historical win-rate, a minimum of 20 trades per phase is required for inclusion, and the result is a historical ranking based on the latest snapshot. It also flags the API Pro tier restriction. It does not cover error cases (e.g., missing phase or insufficient data), but it gives substantive context beyond the schema.

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 longer than a single sentence but each part earns its place: the purpose question, the criteria, the source dependency, the related tools, and the tier note. It is front-loaded with the core question and then provides necessary context. A slightly tighter phrasing could improve it, but it is not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two parameters and no output schema, the description covers what it returns (top-3, sorted by win-rate), the required input (pair), the dependency on the latest phase snapshot, and the inclusion threshold (min 20 trades). It also names related tools, which is contextually rich. The only minor gap is guidance on what happens when no phase or insufficient trade data exists, but given the simple signature, it is adequately complete.

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 coverage is 100% — both pair and asset_type have meaningful descriptions. The description adds marginal value by emphasizing the pair as the core input ('on a given pair') but does not elaborate on asset_type beyond what the enum already provides. Since the schema already documents parameters well, a baseline 3 is appropriate.

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 opens with a clear question that defines the tool's exact purpose: recommending strategies for the current volatility phase, returning top-3 by historical win-rate for that phase on a given pair. It explicitly distinguishes itself from siblings by naming them and their roles (phase itself, filter effects, evidence check), so an agent can differentiate without opening other schemas.

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 states a dependency (phase comes from arena_get_volatility_phases) and lists three related tools with their specific purposes, giving an agent clear context for when to choose this tool over alternatives. It lacks an explicit 'when not to use' statement, but the clear differentiation among siblings effectively guides selection.

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