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i-dream-of-ai

QuantConnect MCP Server

list_projects

Read-only

Retrieve details of all algorithmic trading projects on the QuantConnect platform to manage strategies, backtests, and live deployments.

Instructions

List the details of all projects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorsNoList of errors with the API call.
successNoIndicate if the API request was successful.
projectsNoList of projects for the authenticated user.
versionsNoList of LEAN versions.
Behavior3/5

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

Annotations indicate readOnlyHint=true, which the description aligns with by implying a read operation ('List'). The description adds minimal behavioral context beyond annotations—it specifies 'details of all projects' but doesn't cover pagination, sorting, or response format. With annotations handling safety, this earns a baseline score for slight added value.

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, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it efficient and easy to parse for an AI agent.

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 (0 parameters, read-only per annotations, and an output schema exists), the description is reasonably complete. It states what the tool does, though it could benefit from slight elaboration on usage context. The output schema likely covers return values, reducing the need for more detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, earning a high score as it avoids redundancy and the tool is simple.

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 ('List') and resource ('projects'), specifying it retrieves 'details of all projects'. However, it doesn't differentiate from sibling tools like 'read_project' (which likely fetches a single project) or 'list_backtests'/'list_optimizations' (which list other resource types), missing explicit sibling distinction.

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 doesn't mention when to prefer 'list_projects' over 'read_project' for individual details, or how it relates to other list tools like 'list_backtests', leaving the agent with no usage context.

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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