MCP Server

The following example conversation with Claude Desktop demonstrates how the server equips an LLM with the ability to create trading strategies, backtest them, and deploy them to live trading:

To start using the QC MCP Server, see the Getting Started guide for one of the supported MCP clients.

The following table describes the tools the server provides:

Tool	Cloud	Local
Account
read_account Read the organization account status.
Projects
create_project Create a new project in your default organization.
read_project List details of a project or recent projects.
list_projects List the details of all projects.
update_project Update a project's name or description.
delete_project Delete a project.
Project Collaboration
create_project_collaborator Add a collaborator to a project.
read_project_collaborators List all collaborators on a project.
update_project_collaborator Update collaborator information for a project.
delete_project_collaborator Remove a collaborator from a project.
lock_project_with_collaborators Lock a project so you can edit it.
Project Nodes
read_project_nodes Read the available and selected nodes of a project.
update_project_nodes Update the active state of the given nodes to true.
Compile
create_compile Asynchronously create a compile job request for a project.
read_compile Read a compile packet job result.
Files
create_file Add a file to a given project.
read_file Read a file from a project or all files if no name is provided.
update_file Update the name or content of a file.
update_file_name Update the name of a file.
update_file_contents Update the contents of a file.
patch_file Apply a unified diff patch to a file.
delete_file Delete a file in a project.
Backtests
create_backtest Create a new backtest request and get the backtest ID.
read_backtest Read the results of a backtest.
list_backtests List all backtests for the project.
read_backtest_chart Read a chart from a backtest.
read_backtest_orders Read the orders of a backtest.
read_backtest_insights Read the insights of a backtest.
update_backtest Update the name or note of a backtest.
delete_backtest Delete a backtest from a project.
Optimization
estimate_optimization_time Estimate execution time of an optimization.
create_optimization Create an optimization with parameters.
read_optimization Read an optimization.
list_optimizations List all optimizations for a project.
update_optimization Update the name of an optimization.
abort_optimization Abort an optimization.
delete_optimization Delete an optimization.
Live Trading
authorize_connection Authorize an external live connection.
create_live_algorithm Create a live algorithm.
read_live_algorithm Read details of a live algorithm.
list_live_algorithms List all active and past live deployments.
read_live_chart Read a chart from a live algorithm.
read_live_logs Get logs of a live algorithm.
read_live_portfolio Read the live portfolio state.
read_live_orders Read orders from a live algorithm.
read_live_insights Read insights from a live algorithm.
stop_live_algorithm Stop a live algorithm.
liquidate_live_algorithm Liquidate and stop a live algorithm.
Live Commands
create_live_command Send a command to a live algorithm.
broadcast_live_command Broadcast a live command to all live algorithms.
Object Store
upload_object Upload files to the Object Store.
read_object_properties Read Object Store properties for an organization/key.
read_object_store_file_job_id Create a job to download files and return job ID.
read_object_store_file_download_url Get download URL for Object Store files.
list_object_store_files List Object Store files under a directory.
delete_object Delete an Object Store file.
Lean Versions
read_lean_versions Return a list of LEAN versions.
MCP Server Versions
read_mcp_server_version Return the current MCP Server version.
read_latest_mcp_server_version Return the latest released MCP Server version.
AI
check_initialization_errors Run a short backtest to detect initialization errors.
complete_code Show code completion for specific text.
enhance_error_message Show enhanced error context and suggestions.
update_code_to_pep8 Update Python code to follow PEP8 style.
check_syntax Check syntax of code.
search_quantconnect Search for QuantConnect content.

The following prompts are some ideas on how you can leverage the LLM’s knowledge to improve your workflow:

• Are there any new strategies out there being discussed online?
• How do you think we can improve the Sharpe ratio of this strategy?
• Review my live algorithms. Are there any you think we should stop trading?
• Add Option hedging to my strategy.

The MCP integration supports multiple workflows, ranging from exclusively using the LLM for everything all the way to just prompting the LLM to supplement your activity on our other platforms. The following sections contain several examples of common workflows.

LLM-Focused Workflow

A cloud LLM-focused workflow might look like this:

1. Open a project in Cloud Platform.
2. In the IDE, open the Ask Mia panel.
3. Issue prompts to investigate new trading ideas, create projects, run backtests, analyze backtest performance, and manage live algorithms.
4. When you're finished, close the project.

A local LLM-focused workflow might look like this:

1. Open Docker Desktop and your MCP client application.
2. Issue prompts to investigate new trading ideas, create projects, run backtests, analyze backtest performance, and manage live algorithms.
3. When you're finished, shut down the client and Docker Desktop.

The advantage of an LLM-focused workflow is it doesn't require you to be an expert in programming or financial markets. It leverages the knowledge of the pre-trained LLM to fill your knowledge gaps. The downside to this workflow is LLMs aren't perfect, so you may need to intervene if it starts hallucinating or if it struggles to implement sophisticated trading logic.

Multi-Platform Workflow

A multi-platform workflow might look like this:

1. Open Cloud Platform, Docker Desktop, and your local MCP client application.
2. Create a new project in the Algorithm Lab and run a backtest.
3. Prompt the client application to add a trailing stop order to the project.
4. Deploy an optimization in the Algorithm Lab.
5. Prompt the client application to interpret the optimization results and offer suggestions for improvement.
6. When you’re finished, close the Cloud Platform, client application, and Docker Desktop.

The advantage of this workflow is you can continue using the Cloud Platform, Local Platform, and CLI as you usually would while leveraging the power of LLMs to perform tedious tasks and brainstorm solutions.

There are no quotas on the QuantConnect API, but the client you use may have quotas. For more information about the quotas, see the Quotas section for one of the supported MCP clients.

The following sections explain some issues you may encounter and how to resolve them.

No Local Tools Available

When you open your MCP client, it can take up to 30 seconds for the MCP server to start up and for the client to connect to it. Before you start entering prompts, check Docker Desktop to ensure the container is running and check the client to ensure it’s connected.

If the client can’t discover the tools after 30 seconds, follow these steps:

1. Shut down the client application.
2. If the server doesn’t automatically shut down in Docker Desktop, click the trash icon to shut it down.

3. Restart the client application.

Connection Error Code -32000

The docker run ... command in the configuration file also accepts a --name option, which sets the name of the Docker container when the MCP Server starts running. If your computer tries to start up two MCP Server containers with the same name, this -32000 error occurs. To avoid the error, remove the --name option and its value from the configuration file. For an example of a working configuration file, see the Getting Started guide for one of the supported MCP clients.

Service Outages

The MCP server relies on the QuantConnect API and the client application. To check the status of the QuantConnect API, see our Status page. To check the status of your client and the LLM, see their status page.

The following examples demonstrate the MCP server.

Example 1: Hello World

To test the server and client are working and connected, enter the following prompt into the client application:

> Create a QuantConnect project.

The client should call the create_project tool.

Example 2: Search QuantConnect, Create Projects, and Fix Errors

The following video shows this conversation with Claude Desktop in real-time:

Example 3: Brainstorm Ideas, Add Project Descriptions, and Add Collaborators

The following video shows this conversation with Claude Desktop in real-time: