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by io.github.Schoasch
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Get Indicator Snapshot with Historical Percentile Ranks

arena_get_indicator_snapshot

What do the classic indicators read right now? Current RSI(14), MACD(12/26/9), Bollinger(20,2), ATR(14) and OBV for a pair — each with a PERCENTILE RANK against that indicator's own history on that pair, plus the observation count — the rank turns a raw reading into a placement. ATR comes as a percentage of price so it is comparable across time, and OBV as a 30-bar slope normalised by that window's volume (raw cumulative OBV would mostly rank how long the series has existed). Where the reading sits in an extreme AND a study on this platform has tested that exact state, the payload carries the study verdict — including a null result: a Bollinger squeeze returns the quiet_volatility finding that tight bands did NOT carry an edge. Below 500 bars (1d) / 150 (1w) the raw values still come but percentile is null with a reason, rather than a rounded number from too small a sample. Set interval to '1w' for the weekly view. On the 1d view the payload also carries rsi_14_weekly (weekly RSI with its own rank) — for BTCUSDT this is the SAME series as arena_get_cycle rsi_weekly, measured character-identical (its source_note carries the measurement). It also carries rsi_14_4w (RSI-14 on 28-day candles, with bars — few, so crossing counts stay small; its source_note states what followed crossings). state (oversold/neutral/overbought) names where a reading sits on its own scale. Related: arena_get_trend_channels (structure), arena_get_historical_analog (did a condition like this one ever pay?), arena_get_volatility_history (the volatility series behind ATR). [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pairNoPair, e.g. "BTCUSDT" (default), "ETHUSDT", "PAXGUSDT".
intervalNoDefault '1d'. '1w' computes every indicator on weekly bars.

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: percentile rank methodology, ATR normalization, OBV slope normalization, null percentile thresholds, additional weekly/4w fields, state naming, and the null-result verdict behavior. This is far beyond a generic read snapshot.

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?

Long but information-dense; every sentence adds a distinct behavioral or interpretive fact. Front-loaded with the core question and indicator list, then caveats, then related tools. No filler.

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 2-param read tool with no output schema, this is unusually complete: it covers what is returned, how metrics are computed, when percentile is null, what extra fields appear, and related tools. An agent can call it correctly without further documentation.

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?

Schema already documents pair and interval 100%. Description adds meaning by explaining interval='1w' changes all indicators to weekly bars, and gives example pairs. It also explains the threshold behavior tied to interval, so it adds value beyond the 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 concrete question and enumerates the exact indicators (RSI(14), MACD(12/26/9), Bollinger(20,2), ATR(14), OBV) plus percentile ranks, making the resource and scope unmistakable. It also distinguishes itself from sibling tools by naming related tools and their different purposes.

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?

It explicitly lists related tools with parentheticals explaining what each is for, and tells the user to set interval='1w' for the weekly view. It doesn't provide strict when-not-to-use rules, but the context is clear enough to route an agent.

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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