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Backtesting Arena

by io.github.Schoasch

Get Spot-ETF Net-Flow Trend (BTC / ETH / SOL)

arena_get_etf_flows

Spot-ETF net flows (USD millions) — is the flow impulse turning or accelerating? The summary only gives point-in-time deltas; this exposes the trend: 30d/90d net flow, a direction label (inflows/outflows/flat) and a compact cumulative-inflow time series so direction and speed are visible, not just a single delta. Read impulse for what the flow is doing — it has four states (accelerating / decelerating / reversal / flat) and is the field to quote. Two neighbouring fields measure different things and are easy to confuse: acceleration_usd_m is the signed difference last-30d minus prior-30d and gets LARGE precisely when the flow reverses, while the older boolean accelerating requires the same direction AND a bigger magnitude — so a swing from outflows to inflows shows a big positive acceleration_usd_m together with accelerating: false, which is correct and reads like a contradiction. impulse reports that case as 'reversal'. When impulse is 'reversal', reversal_recovered_pct says how much of the preceding counter-move has actually come back, with its denominator in reversal_basis_usd_m — quote it alongside, because a reversal in direction is not yet a reversal in the stock. Both are null otherwise. Default BTC; pass asset=ETH or asset=SOL. Source SoSoValue. [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLength of the returned cumulative series in days. Default 365, clamped 90–1095.
assetNoWhich spot-ETF flows. Default BTC.

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. Added

TDQS

A4.2/5.0
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 excels. It thoroughly explains the interpretation of key fields, warns about the confusing difference between `acceleration_usd_m` and `accelerating`, details the four states of `impulse`, and clarifies how to read reversal metrics. This is rich behavioral context that prevents misinterpretation.

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 notably long but deliberately structured: it front-loads the purpose, then systematically explains `impulse`, the confusion between `acceleration_usd_m` and `accelerating`, and the reversal fields. Each sentence serves a purpose—no filler—but the length is above typical and could be tightened. It earns a 4 for being information-dense without redundancy.

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 there is no output schema, the description must explain return fields and semantics, and it does so comprehensively. It covers the `impulse` states, the acceleration metrics, reversal recovery, defaults, and source. An agent has everything needed to call the tool and interpret results correctly, including edge cases like the apparent contradiction.

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?

The schema covers both parameters with defaults and enums (days: clamp 90-1095, asset: BTC/ETH/SOL). The description merely echoes these defaults ('Default BTC; pass asset=ETH or asset=SOL') without adding new semantic meaning. With 100% schema coverage, baseline 3 applies; no supplementary value beyond schema.

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 opens with a precise statement of what the tool does: 'Spot-ETF net flows (USD millions) — is the flow impulse turning or accelerating?' and clarifies it exposes the trend rather than a point-in-time delta. This clearly identifies the resource (ETF flows) and the specific value it adds, making it easily distinguishable from the many other arena_get_* indicator tools.

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 implied usage guidance by contrasting with a 'summary' that only gives point-in-time deltas, suggesting this tool is for trend analysis. However, it does not explicitly name alternative tools or state when NOT to use this tool. The guidance is contextual but lacks explicit exclusions or sibling routing.

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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