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Backtesting Arena

by io.github.Schoasch

Get Crypto Cycle Snapshot (BTC / ETH / SOL)

arena_get_cycle

Crypto cycle position — where are we in the cycle? Default BTC: point-in-time 9-indicator aggregation (Pi-Cycle Top & Bottom, Mayer Multiple, weekly RSI, 200-week-MA distance, halving position, Fear & Greed, BTC-dominance trend, mining-difficulty trend — weights in indicator_scores; components without input are excluded and weights renormalized, see indicator_coverage). Includes an ath block (E32): ATH on UTC daily-close basis with ath_date, days_since_ath and drawdown_from_ath_pct vs BOTH the scoring price and the live spot. Pass asset=ETH or asset=SOL for a per-coin cycle read built from the transferable price-derived indicators (Mayer, weekly-RSI, 200-week-MA distance) with renormalized weights; BTC-native indicators (halving, dominance, mining, F&G, Pi-Cycle) are returned as not_applicable rather than faked. All return raw + Z-Score, signal enum, and a percentiles block ranking each indicator against that asset’s own history. The signal enum is a FIXED SCORE-BAND LABEL (<25 accumulation · 25–45 recovery · 45–60 expansion · 60–75 distribution · ≥75 overheated), not an independent market-phase detection: the 45–60 band is the neutral middle, so a mid-band score reads "expansion" even in a drawdown market — the label describes the score band, not the market. BTC additionally returns highlights[] (rule-based markers for currently unusual indicator values — descriptive, versioned ruleset; empty array = nothing unusual) and price_context (price at scoring time vs live spot with drift % — the scores rest on the scoring-time price). Point-in-time scored — not reconstructable from a generic price API. The volatility series itself is arena_get_volatility_history; this tool carries the regime context around it. Related: arena_get_historical_analog (what followed states like this one), arena_get_bullmarket_ampel, arena_get_pulse. [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetNoWhich asset’s cycle. Default BTC. ETH/SOL return a price-derived cycle read with not_applicable fields for BTC-native indicators.

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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, and it does so thoroughly. It states that scoring is point-in-time and not reconstructable from a generic price API, explains that components without input are excluded and weights renormalized (with indicator_coverage), details the ath block calculations, clarifies the signal enum is a fixed score-band label rather than an independent phase detection, and discloses BTC-specific highlights and price_context. It also transparently mentions that BTC-native indicators are returned as not_applicable for ETH/SOL rather than faked. This level of detail precisely informs an agent about side effects and limitations.

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 long but information-dense, with every sentence contributing to behavioral understanding. It is front-loaded with the core purpose and then progressively details variants and caveats. While it could be trimmed slightly without losing essential meaning, the length is justified given the tool's complexity and the need to disclose multiple behaviors (indicator handling, signal label semantics, asset-specific differences). The structure is logical, moving from core functionality to edge cases.

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 tool with no output schema and one optional parameter, the description covers all necessary aspects an agent needs to call it correctly: what the tool returns (indicator_scores, ath block, percentiles, signal enum, highlights, price_context), how it handles different assets, how scoring works (point-in-time, renormalization), and its relationship to sibling tools. It even explains the signal enum's semantics to prevent misinterpretation. Nothing critical is missing for the agent to make an informed call.

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 single parameter `asset` is fully documented in the schema (100% coverage), so the baseline is 3. The description adds significant meaning beyond the schema by elaborating on what each asset value does: ETH/SOL return a price-derived cycle read built from transferable indicators with renormalized weights, and BTC-native indicators are returned as not_applicable. It also explains the indicator composition for BTC and how weights are handled, which is not in the schema. This adds real interpretative value beyond the enum description.

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 a specific purpose: providing a crypto cycle snapshot with a point-in-time 9-indicator aggregation for BTC and a per-coin price-derived read for ETH/SOL. It differentiates from siblings by naming related tools (arena_get_volatility_history, arena_get_historical_analog, etc.) and stating what this tool specifically does differently, such as carrying regime context around the volatility series. The verb 'get' plus the resource 'cycle snapshot' is unambiguous.

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 when to use this tool by explaining its scope (cycle position snapshot) and mentioning related tools for alternative use cases (e.g., arena_get_historical_analog for what followed similar states, arena_get_volatility_history for the volatility series). However, it does not explicitly state scenarios where this tool should not be used or give a clear conditional like 'use this when you need X, otherwise use Y'. The guidance is implicit rather than explicit, so it misses the top score.

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.

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