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invent_and_backtest

ONE-SHOT public exploration: generate ArenaScript, validate, then backtest with the Lab Rust engine on server OHLCV. Returns script + validation + metrics so an AI can discover quickly. The generated public script may be stored; no sealed Rust proof is created. style: balanced|trend|breakout|momentum|mean_reversion|scalping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ideaYesIdée de stratégie en texte libre (2 000 caractères max).
assetNoActif, ex. BTC, ETH, SOL, BNB.BTC
styleNomean_reversion
timeframeNo5m, 10m, 15m, 30m, 1h, 2h, 4h ou 1d.10m

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations only give the coarse safety profile (readOnlyHint=false, non-idempotent, non-destructive); the description supplies the crucial specifics: the generated script may be stored/persisted and no sealed Rust proof is created. It also states the return payload. It does not mention latency, cost, or limits, so it is useful but not exhaustive.

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?

Four tight sentences, front-loaded with the pipeline and outcome, each carrying distinct information (operation, persistence caveat, proof limitation, style values). Slightly telegraphic phrasing ('script + validation + metrics') but no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-parameter one-shot pipeline whose output schema exists, the description covers the operation, side effects, and the style vocabulary an agent needs. Remaining gaps are operational (runtime, rate limits, whether re-running overwrites a stored script), which are minor against the available structured data.

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 75% and the 'style' parameter has no enum and no schema description; the description supplies the full accepted set (balanced|trend|breakout|momentum|mean_reversion|scalping), which is genuine added value. It adds nothing for idea/asset/timeframe, but those already carry schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific compound operation (generate ArenaScript, validate, then backtest on server OHLCV) and its scope as 'ONE-SHOT public exploration'. It doesn't explicitly name the sibling pipeline tools (generate_arenascript_strategy, validate_arenascript, backtest_arenascript) that it collapses, but the 'ONE-SHOT' framing lets an agent infer it is the fused alternative.

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?

'so an AI can discover quickly' implies this is the fast exploratory path versus the individual step tools, and 'public' implies a non-production context. However, no explicit when-to-use/when-not comparison to the sibling backtest or generate tools is stated, leaving the choice largely to inference.

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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