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Learned Combo Win Rates

learned_combos
Read-onlyIdempotent

Show win rates for combinations of indicators that fire together, such as RSI overbought plus Bollinger upper. Use this data to identify high-edge setups during specific market regimes.

Instructions

Show COMBINATION signal win rates learned from live observation, cases where multiple indicators fire together (e.g. RSI-overbought + Bollinger-upper) in a given market regime (fear/neutral/greed). Combos usually carry a stronger edge than any single signal, and the setup scanner automatically boosts picks that match a high-win-rate combo. Empty until enough co-firing signals are graded; fills as the bot runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds useful behavioral context beyond the annotations by explaining that the results are empty until enough co-firing signals are graded and fill as the bot runs. This aligns with the openWorldHint and idempotentHint annotations without contradiction.

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 three sentences long, with each sentence adding value: purpose, significance/usage, and data availability. It is front-loaded with the main purpose, though the comparative sentence about scanner boosting could be seen as slightly tangential.

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 simple tool with one optional parameter and an output schema, the description covers the core purpose, market-regime context, and data-fill behavior. The main gap is the omitted explanation of the 'top' parameter, which slightly reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'top' with 0% description coverage, and the description does not mention it at all. The meaning of 'top' (likely top N combos) is left to inference from the name and default, which is insufficient given low 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 shows COMBINATION signal win rates learned from live observation, using the specific verb 'Show' and resource 'combination signal win rates'. It also distinguishes from single-signal tools by emphasizing multiple indicators firing together (e.g., RSI-overbought + Bollinger-upper).

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?

The description implies usage context by noting combos usually carry a stronger edge than any single signal and that the setup scanner boosts high-win-rate combos. However, it does not explicitly name alternatives like 'learned_winrates' or state when not to use the tool, so it lacks explicit 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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