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Backtesting Arena

by io.github.Schoasch

Get Data-Quality Drift Log

arena_get_drift_log

Do two independent providers still agree on the same on-chain quantity? Daily drift log comparing BRK against a second provider, per metric pair, with mean / max / outlier counts for the requested window. The comparison source changed — bgeometrics until 2026-07-06, Coinmetrics from 2026-09 — so every row and summary group carries comparison_source, and values from different sources must not be averaged together. Active pairs: market_cap, circulating_supply, hash_rate, puell_multiple. Retired but still queryable as history: realized_price, sth_sopr, lth_sopr. Passive collection: drift never auto-switches a source. [API Pro tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoWindow size in days (default 90, max 365).

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

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries full behavioral burden and does so excellently. It discloses the comparison source change with exact dates, warns that rows from different sources must not be averaged, states that drift never auto-switches source, and notes the API tier requirement. This is rich, non-obvious behavior an agent needs to know.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, source-change caveat, active/retired pairs, passive behavior, and access tier. Important warnings are front-loaded near the purpose statement, and there is no redundant or filler text.

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 single-parameter tool with no output schema, the description gives sufficient detail for correct invocation: what is returned, which metrics are currently active, which are historical, and the critical source-averaging constraint. An agent can select and call this tool correctly without further information.

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% and the only parameter, days, is already documented with default and max. The description's mention of the 'requested window' adds no new semantic detail beyond what the schema provides. This is the appropriate baseline for high schema coverage.

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: comparing BRK against a second provider to detect data-quality drift, with mean/max/outlier counts. It uniquely identifies the resource (drift log) and distinguishes it from the many other indicator tools in the sibling list. The active and retired metric pairs add further specificity.

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 gives clear context about what the tool covers — daily drift, specific metric pairs, and historical queryability — so an agent can infer when to use it. However, it does not explicitly state when to prefer this tool over alternatives like arena_get_onchain_history or arena_get_onchain_latest, and it names no exclusions.

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