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by io.github.Schoasch
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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. score_fields_note explains the four score fields: z_score/z_adj_score are the composite standardized against its own history and mapped back onto the 0-100 scale, NOT statistical z-values; halving_context.ath_days_after_halving puts the observed cycle high next to days_since_halving. 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.

  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

A4.9/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden, and it excels. It discloses the aggregation logic (9 indicators, weights, renormalization when components are missing), the ATH block details, the per-coin behavior for ETH/SOL (returning not_applicable for BTC-native indicators), the signal enum being a fixed score-band label rather than market-phase detection, the highlights and price_context blocks, and the fact that scores are point-in-time and not reconstructable. It even explains the z-score fields and the halving_context field. This is exemplary transparency.

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 every sentence carries meaningful information. It is structured with a clear lead-in (core purpose), then detailed blocks for ATH, per-coin behavior, signal enum caveat, highlights, price_context, and related tools. The most critical facts (default asset, indicator aggregation, signal meaning) are front-loaded. It is dense but not redundant; it earns its length, though it could be tightened slightly without losing value.

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?

Given the complexity of the tool and the lack of an output schema, the description is remarkably complete. It explains what is returned (raw + Z-Score, signal enum, percentiles, ATH block, highlights, price_context), the meaning of the signal enum, the scoring methodology, and the relationship to other tools. It also covers edge cases (excluded indicators, not_applicable fields) and the non-reconstructability of scores. There is no missing information that would prevent correct invocation.

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?

The schema covers the single parameter (asset) with an enum and a brief description. The tool description adds substantial meaning: it explains the default value (BTC), the different behavior for ETH/SOL (price-derived indicators vs. not_applicable for BTC-native ones), and the implications of choosing each asset. This goes well beyond the schema's 'Which asset’s cycle' and provides context that helps the agent select the correct parameter.

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 what the tool does: 'Crypto cycle position — where are we in the cycle?' and specifies the default asset (BTC), the 9-indicator aggregation, and the optional per-coin read for ETH/SOL. It distinguishes from siblings like arena_get_cycle_history and arena_get_historical_analog by focusing on the current cycle snapshot rather than history or analog states. The verb is specific ('get'), the resource is unambiguous, and the scope (point-in-time aggregation) is explicit.

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 explicitly names related tools and their purpose: 'Related: arena_get_historical_analog (what followed states like this one), arena_get_bullmarket_ampel, arena_get_pulse.' It also clarifies a key distinction: 'The volatility series itself is arena_get_volatility_history; this tool carries the regime context around it.' This tells the agent when to use this tool vs. alternatives, and what it does not cover. The guidance is direct and actionable.

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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