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i-dream-of-ai

QuantConnect MCP Server

read_live_portfolio

Retrieve current portfolio status from a live trading algorithm on QuantConnect, with updates approximately every 10 minutes.

Instructions

Read out the portfolio state of a live algorithm.

The snapshot updates about every 10 minutes.

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.
portfolioNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the data updates every 10 minutes, which is useful behavioral context about freshness. However, it doesn't mention permissions, rate limits, or what the output contains (though an output schema exists, reducing this burden).

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 appropriately sized with two sentences. The first sentence states the purpose, and the second adds valuable behavioral context about update frequency. There is no wasted text, and it's front-loaded with the core action.

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 the tool has an output schema (which handles return values) and no annotations, the description is minimally adequate. It covers the basic purpose and a key behavioral trait (update frequency), but lacks parameter details and usage guidelines, leaving gaps for an agent to infer correct invocation.

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?

Schema description coverage is 0%, so the description must compensate. It adds no information about the single parameter 'projectId' beyond what the schema minimally provides (type and requirement). No examples, constraints, or meaning of the parameter are explained in the description.

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 ('Read out') and resource ('portfolio state of a live algorithm'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'read_live_algorithm' or 'read_live_insights', which might provide related but different data about live algorithms.

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 mentions the snapshot updates every 10 minutes, which hints at usage timing, but doesn't specify prerequisites, exclusions, or compare to other read tools for live algorithms in the sibling list.

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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