Skip to main content
Glama
QuantConnect

QuantConnect

Official
by QuantConnect

create_optimization

Set up a parameter optimization for a QuantConnect algorithm to maximize or minimize a target statistic like Sharpe ratio by defining parameters, constraints, and resource allocation.

Instructions

Create an optimization with the specified parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
optimizationsNoCollection of summarized optimization objects.
successNoIndicate if the API request was successful.
errorsNoList of errors with the API call.
Behavior2/5

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

The description does not disclose behavioral traits beyond the annotation (destructiveHint=false). It does not mention idempotency, side effects, or rate limits. For a creation tool, more context is needed, such as whether it replaces an existing optimization or creates a new one.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise, but it lacks structure and is under-specified for the tool's complexity.

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 input schema with many required nested fields and the existence of sibling tools, the description is insufficient. It does not provide context about what an optimization is, how to configure it, or what the output will be.

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 description adds no meaning beyond the schema. The top-level parameter 'model' has no description (0% coverage), and the description does not explain the required nested fields or any constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the verb 'create' and resource 'optimization', making the basic action clear. However, it does not distinguish from sibling tools like 'update_optimization' or 'delete_optimization', and the phrase 'specified parameters' is vague.

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. There is no mention of prerequisites, context, or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/QuantConnect/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server