TradeMemory Protocol

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evolution_evolve_strategy

Evolve trading strategies over generations: generate candidates, backtest in-sample, validate out-of-sample, eliminate weak hypotheses, and return surviving strategies.

Instructions

Run full evolution loop — generate, backtest, select, eliminate.

Multi-generation strategy evolution: generates candidate patterns via LLM, backtests on in-sample data, validates survivors on out-of-sample data, eliminates weak hypotheses. Returns graduated strategies and graveyard.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "evolution_evolve_strategy"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	Days of history to use (default 90)
`symbol`	Yes	Trading pair (e.g. "BTCUSDT")
`timeframe`	No	Bar timeframe — "5m", "15m", "1h", "4h", "1d"	1h
`generations`	No	Number of evolution generations (default 3)
`population_size`	No	Hypotheses per generation (default 10)

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.5.4Aug 2, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations are all false, providing minimal behavioral signal. The description adds useful process details (LLM generation, in-sample/out-of-sample validation, elimination of weak hypotheses) and return value ('graduated strategies and graveyard'). It does not disclose potential side effects like resource usage or persistence, but it does not contradict the annotations.

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?

The description is front-loaded with a one-line summary ('Run full evolution loop — generate, backtest, select, eliminate.') followed by a concise elaboration. It contains no fluff and every sentence contributes to understanding the tool's behavior.

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?

The description covers the core workflow, data splits, and return value, and an output schema exists so return format details are covered. It does not mention prerequisites or how it relates to sibling tools (e.g., whether it requires evolution_fetch_market_data first), but for a complex pipeline the description is sufficiently comprehensive.

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 input schema has 100% parameter description coverage, so the schema already explains every parameter. The description adds no direct parameter-level meaning beyond the schema, making the baseline 3 appropriate.

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 explicitly states 'Run full evolution loop' and lists the specific steps (generate, backtest, select, eliminate), making the tool's purpose unmistakable. It also differentiates itself from sibling tools like evolution_run_backtest and evolution_discover_patterns by focusing on the complete multi-generation pipeline rather than individual steps.

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 clearly defines the tool as a full-loop process, implying it should be used when the complete evolution workflow is needed. However, it does not explicitly mention alternatives or say 'use evolution_run_backtest for single backtests,' so it lacks explicit exclusion guidance but provides strong contextual clues.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.