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AlgoChains

AlgoChains MCP Server

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by AlgoChains

optimize_strategy

Idempotent

Optimize trading strategy parameters using Optuna. Run multiple trials to find the best combination based on a chosen metric like Sharpe.

Instructions

Run Optuna-based parameter optimization on a StrategySpec. Finds best params across n_trials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specYes
metricNosharpe
n_trialsNo
Behavior2/5

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

Annotations already indicate idempotentHint=true and destructiveHint=false, so the description adds little beyond confirming it is a non-destructive optimization. It does not disclose side effects such as whether results are saved, how outputs are returned, or any resource state changes.

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 very short (two sentences) and front-loaded with the core action. While concise, it sacrifices important details, but the structure itself is efficient.

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 complexity of the tool (nested object parameter, multiple parameters, no output schema), the description is too sparse. It does not explain what 'spec' should contain, what the metric parameter affects, or what output the agent should expect. The agent cannot determine how to structure the input or interpret results.

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?

With 0% schema description coverage, the description must compensate, but it only partially explains 'spec' and 'n_trials' via context. The 'metric' parameter is completely omitted, leaving its purpose and accepted values unclear. This is insufficient for correct invocation.

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?

The description clearly states the verb 'optimize' and the resource 'StrategySpec', specifying the method 'Optuna-based' and goal 'find best params across n_trials'. However, it does not differentiate from sibling tools like 'run_backtest' or 'validate_strategy', which are related but distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, trade-offs, or cases where another tool would be more appropriate. An agent would have no basis to choose this over sibling tools.

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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