MCP Server

The following conversation with Copilot Chat in Local Platform 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 server provides the following tools:

Projects

• read_open_project - Read the project that's currently open.
• update_project - Update a project's name or description.

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 in a project.
• delete_project_collaborator - Remove a collaborator from a project.

Project Nodes

• read_project_nodes - Read the available and selected nodes of a project.

Compile

• create_compile - Compile the project to check for syntax errors, compilation errors, and other issues.

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 the backtests for the project.
• update_backtest - Update the name or note of a backtest.
• delete_backtest - Delete a backtest from a project.

Optimization

• create_optimization - Create an optimization with the specified parameters.
• read_optimization - Read an optimization.
• list_optimizations - List all the optimizations for a project.
• abort_optimization - Abort an optimization.
• delete_optimization - Delete an optimization.

Live Trading

• create_live_algorithm - Create a live algorithm.
• read_live_algorithm - Read details of a live algorithm.
• stop_live_algorithm - Stop a live algorithm.
• liquidate_live_algorithm - Liquidate and stop a live algorithm.

Research

The following tools operate on a Jupyter notebook in the QuantConnect Cloud Research environment:

• jupyter_create_cell - Insert a new Jupyter notebook cell. By default the cell is appended; provide an index to insert at a specific zero-based position.
• jupyter_read_cell - Return the source lines for a single notebook cell at a zero-based index.
• jupyter_update_cell - Replace the source of an existing notebook cell at a zero-based index.
• jupyter_delete_cell - Delete a notebook cell at a zero-based index.
• jupyter_execute_cell - Execute one code cell by zero-based index and return its captured outputs.
• jupyter_create_notebook - Replace the current notebook with raw notebook JSON content.
• jupyter_read_notebook - Return the full current notebook as a raw JSON string.
• jupyter_execute_notebook - Execute all notebook cells in order and return aggregated outputs from code cells.

News

The following tools fetch financial news and blog posts from a curated list of more than 20 sources:

• financial_data_blog_posts - Fetch recent financial blog posts.
• financial_data_web_get - Fetch the full content of a web page given its URL. Use this to read blog posts and articles.

Environment

• environment_library_support - Fetch the list of available preinstalled libraries in the QuantConnect environment.
• datasets - Fetch the list of available QuantConnect datasets.

Notifications

The following tools send messages from QuantConnect Cloud to you using the Live Trading Notifications system:

• send_email_notification - Send an email notification.
• send_sms_notification - Send an SMS notification.
• send_telegram_notification - Send a Telegram notification.

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 server is available in QuantConnect Cloud and in some local IDEs.

Cloud Workflow

A cloud 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.

Local Workflow

A local workflow might look like this:

1. Open Local Platform, Cursor, or Windsurf.
2. Open a project and connect to the MCP Server.

For specific instructions on each IDE, open the MCP Server documentation, click a client application, and scroll down to the Getting Started section.

3. In the chat window, 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.

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.

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:

> Read the open project.

The agent should call the read_open_project tool.

Example 2: Strategy Development and Deployment

This example uses Copilot Chat in Local Platform to brainstorm new strategy ideas, edit files, run backtests, and deploy to paper trading.