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QuantConnect MCP Server

read_live_logs

Retrieve real-time logs from a live trading algorithm to monitor performance and debug issues, with updates every 5 minutes.

Instructions

Get the logs of a live algorithm.

The snapshot updates about every 5 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
logsNoList of logs from the live algorithm.
errorsNoList of errors with the API call.
lengthNoTotal amount of rows in the logs across all live deployments for this project.
successNoIndicate if the API request was successful.
deploymentOffsetNoNumber of log rows before the given deployment (the `algorithmId` in the request).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description adds one behavioral trait: snapshot updates every 5 minutes. However, it omits other important details like authorization requirements or data staleness implications.

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?

Two efficient sentences conveying the purpose and update frequency with no redundancy. Every sentence adds value.

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

Completeness4/5

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

Given the tool's simplicity and the presence of an output schema, the description adequately covers purpose and an important timing behavior. Minor missing context about how to ensure fresh logs, but overall sufficient.

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?

The input schema already provides thorough descriptions for all parameters (projectId, algorithmId, startLine, endLine, format). The description adds no further parameter guidance, so baseline 3 is appropriate.

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?

Description clearly states 'Get the logs of a live algorithm,' which is a specific verb+resource. It distinguishes from sibling tools that deal with other aspects of 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 Guidelines3/5

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

The description implies usage when logs are needed but provides no explicit guidance on when to use this tool versus alternatives like read_live_algorithm or read_live_chart.

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