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AlgoChains

AlgoChains MCP Server

Official
by AlgoChains

run_evolution_cycle

Idempotent

Optimize a trading strategy by scanning underperformers, mutating parameters with Optuna, validating against trade history, and promoting the winner using reinforcement learning.

Instructions

Trigger an AlphaLoop evolution cycle: SCAN underperformers → MUTATE parameters via Optuna → VALIDATE against real trade history → PROMOTE winner. Uses RL reward model. Requires real trade history (min 5 trades).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
generationsNo
strategy_idYes
promote_thresholdNoMin reward improvement to promote
min_trades_requiredNo
Behavior3/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds context about the multi-step cycle and RL model, but does not disclose side effects (e.g., whether parameters are permanently modified) or confirm idempotency. It provides moderate additional transparency beyond 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 only two sentences with no filler. The first sentence clearly lists the sequence in a front-loaded manner. Every word serves a purpose, making it highly concise and well-structured.

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

Completeness2/5

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

Given the absence of an output schema, the description should indicate what the tool returns (e.g., a task ID or success message) or provide guidance on interpreting results. It only describes the process and a prerequisite, leaving the agent without critical information on how to use the tool's output. This is a significant gap for a 4-parameter tool.

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

Parameters1/5

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

Schema coverage is only 25% (only 'promote_threshold' has a description). The description fails to explain any of the four parameters, including 'strategy_id', 'generations', and 'min_trades_required'. It mentions 'min 5 trades' but the parameter 'min_trades_required' defaults to 10, introducing inconsistency. The description adds virtually no value to parameter understanding.

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 specifies a clear verb ('Trigger') and resource ('AlphaLoop evolution cycle'), and breaks down the process into four distinct steps (SCAN, MUTATE, VALIDATE, PROMOTE). It also notes the use of an RL reward model, setting it apart from generic optimization tools. This level of detail provides excellent purpose clarity.

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?

The description states a prerequisite ('Requires real trade history (min 5 trades)') but does not explain when to prefer this tool over siblings like 'optimize_strategy' or 'run_backtest'. There is no guidance on scenarios where this tool is inappropriate or on alternatives, resulting in moderate usage guidance.

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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