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Backtesting Arena

by io.github.Schoasch

Get Bitcoin Halving History

arena_get_halvings

When were the halvings, and what followed? The full Bitcoin halving table: date, block height, reward change, price at the halving day, cycle peak (highest daily close before the next halving) with days-after-halving and peak multiple. E33: this connects chain events with price data no generic price API links — and the prices, peaks and days-to-peak are COMPUTED from our own daily-close series rather than hand-copied (public "days to peak" figures often mix cycles: the widely cited 547-549 days belong to cycle 3, not cycle 4). The date convention is declared per entry (block 840,000 fell 2024-04-20 00:09 UTC; this platform counts from 2024-04-19 — same convention as arena_get_cycle.halving_date). The current cycle's peak is marked provisional. No next-halving date estimate is published. Related: arena_get_cycle (days_since_halving, score_halving). [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. Added

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description fully discloses behavior: data is computed from own daily-close series, date conventions are declared per entry, the current cycle peak is marked provisional, and no next-halving estimate is published. It even warns about a common data-mixing pitfall. This is comprehensive 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 information-dense. It fronts the core output ('full Bitcoin halving table') and then adds useful clarifications about data provenance and conventions. While it could be tightened, every sentence adds value or prevents misinterpretation.

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 parameters and no output schema, the description is exceptionally complete. It covers what data is returned, how it is computed, the date convention, the provisional status, and a missing feature. An agent would know exactly what to expect and how to interpret results.

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?

With zero parameters, the baseline is 4. The description adds context about the data source and conventions, which is helpful even though no parameter documentation is needed. It does not repeat schema information since there is none.

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 returns a full Bitcoin halving table with specific fields (date, block height, reward change, price, cycle peak). It explicitly differentiates itself from generic price APIs and references a related tool (arena_get_cycle) to avoid confusion. The verb and resource are 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 implies its use case (when you need halving dates and associated price data) and mentions related tools (arena_get_cycle) as alternatives. It also states a limitation ('No next-halving date estimate is published') which indirectly signals when not to rely on it. However, it lacks explicit when-to-use vs. when-not-to-use conditions.

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