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i-dream-of-ai

QuantConnect MCP Server

create_backtest

Generate backtest results for algorithmic trading strategies by submitting project data and parameters to test performance against historical market data.

Instructions

Create a new backtest request and get the backtest Id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorsNoList of errors with the API call.
successNoIndicate if the API request was successful.
backtestNoDetails on the result of the backtest.
debuggingNoIndicates if the backtest is run under debugging mode.
Behavior3/5

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

Annotations indicate 'destructiveHint: false', which the description doesn't contradict (it describes a creation operation, not destruction). However, the description adds minimal behavioral context beyond annotations—it mentions getting a backtest ID as output, but doesn't cover aspects like rate limits, authentication needs, or what happens if the backtest fails.

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 a single, efficient sentence that front-loads the key action and outcome. There's no wasted verbiage, making it easy to parse quickly.

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

Completeness3/5

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

Given that there's an output schema (which likely covers the backtest ID), the description doesn't need to detail return values. However, for a creation tool with 0% schema coverage and no behavioral annotations beyond destructiveness, the description is too sparse—it doesn't explain prerequisites, error conditions, or how it relates to sibling tools, leaving gaps in understanding.

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?

Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description provides no information about parameters, not even hinting at required fields like 'projectId' or 'compileId'. With 1 parameter (a nested object), the baseline is 4 for zero parameters, but here the description fails to compensate for the lack of schema documentation.

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 action ('create a new backtest request') and the outcome ('get the backtest Id'), which distinguishes it from sibling tools like 'list_backtests' or 'read_backtest'. However, it doesn't specify what a 'backtest' is in this context, which could help differentiate from similar tools like 'create_optimization'.

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?

No guidance is provided on when to use this tool versus alternatives like 'create_optimization' or 'create_live_algorithm', nor are prerequisites mentioned (e.g., needing a compiled project). The description only states what it does, not when it's appropriate.

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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