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Backtesting Arena

by io.github.Schoasch

Get BTC × Macro Correlations

arena_get_btc_macro_correlations

What does Bitcoin actually move with? Pre-aggregated weekly correlations between Bitcoin and 13 macro components (Fed Net Liquidity, VIX, DXY, Real Yield 10Y, NFCI, Yield Curve, etc.). Returns quadrant_performance (BTC return stats per 2D-matrix quadrant — annualized return, vol, max drawdown, positive-period%), component_correlations (Pearson 90d/1y/5y per macro component + quartile-performance), asset_correlations (Pearson per window + per quadrant; assets: dxy plus tokenized on-venue proxies paxg = PAX Gold, spyb = S&P 500 ETF proxy, qqqb = Nasdaq-100 ETF proxy — proxies carry tracking noise vs. the underlying, and windows the vehicle history does not cover are null with data_start_date telling you why: the ETF proxies listed on Binance mid-2026, so their windows fill in over time — 90d first, ~2 months after listing), current_quadrant. Window labels are upper bounds — sample_size_days / data_start_date carry the actual basis. Historical analysis over the windows named above. [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed2 schema fields changed
    • 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. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It is highly transparent: explains proxy tracking noise for ETFs, null windows due to listing dates and how they fill in, and clarifies that window labels are upper bounds with data_start_date carrying the actual basis. It also indicates 'Free tier' status. It could mention read-only nature explicitly, but since it's a 'get' tool, the absence is not a major gap.

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 typical but each sentence contributes meaning: it front-loads the purpose, then enumerates the return components, and covers caveats about proxies and window validity. It is well-structured and not verbose for the complexity it handles. The information is dense yet organized.

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 parameterless tool with no output schema and no annotations, the description is exceptionally complete. It explains all return structures, data provenance, proxy tracking noise, null-handling logic, and window-label semantics. An agent has everything needed to know what to expect and how to interpret results, making it self-sufficient.

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?

The tool has zero parameters, triggering the baseline of 4. The description does not need to explain parameters, and it adds value by detailing the output structures and their semantics, which is beyond parameter coverage. Since there are no parameters, it cannot add parameter-specific meaning, but the baseline applies.

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's purpose: pre-aggregated weekly correlations between Bitcoin and 13 macro components, listing examples and detailing the returned structures (quadrant_performance, component_correlations, asset_correlations, current_quadrant). This is a specific verb+resource combination that distinguishes it from other arena_* tools, which cover different analyses (e.g., sentiment, cycles, volatility).

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

Usage Guidelines3/5

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

The description provides clear context about what the tool does, but does not explicitly state when to use it versus alternatives. It implies usage for understanding Bitcoin's macro correlations, but there are no exclusions or references to sibling tools. Since it's a specialized tool with a clear domain, the implied guidance is adequate but not explicit.

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