Backtesting Arena

Get Indicator Snapshot with Historical Percentile Ranks

arena_get_indicator_snapshot

Read-only

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]

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "arena_get_indicator_snapshot"
  }
}

Input Schema

Name	Required	Description	Default
`pair`	No	Pair, e.g. "BTCUSDT" (default), "ETHUSDT", "PAXGUSDT".
`interval`	No	Default '1d'. '1w' computes every indicator on weekly bars.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 29, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```

Changed3 schema fields changedAug 25, 2026

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"
-]
```

AddedAug 9, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With readOnlyHint already covering safety, the description adds substantial beyond-annotation detail: percentile is null below 500 daily / 150 weekly bars with a stated reason, ATR is expressed as % of price to be time-comparable, OBV is a 30-bar slope normalised by window volume, and extreme readings carry study verdicts including null results. That is exactly the disclosure an agent needs to interpret the payload.

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

Conciseness3/5

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

Purpose is front-loaded in the first clause, but the body is a dense run-on with heavy parentheticals (source_note provenance, bars caveats) that outsize a two-parameter read tool. Much of it earns its place, yet the provenance meta-commentary could be trimmed.

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?

There is no output schema, and the description compensates by naming the returned fields (percentile, observation count, rsi_14_weekly, rsi_14_4w, state, study verdicts) and their edge-case behavior. Nothing an agent needs to call or interpret this tool is missing.

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 coverage is 100%, so the baseline is 3, but the description adds real semantics: '1w' recomputes every indicator on weekly bars and carries a different minimum-bar threshold, and pair defaults are implied by the example. It goes modestly beyond the schema text.

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 opening question plus the explicit enumeration of RSI(14), MACD(12/26/9), Bollinger(20,2), ATR(14) and OBV with percentile ranks states a specific verb and resource set. It is clearly separable from siblings like arena_get_volatility_history or arena_get_asset_snapshot, which cover different data.

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 gives concrete context (current classic-indicator readings with placement against own history) and routes the agent elsewhere for adjacent needs: trend_channels for structure, historical_analog for conditional payoff, volatility_history for the ATR series. No explicit when-not-to-use exclusion, but the sibling routing is clear.

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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