Using MCP in Thunderflow AI

Model Context Protocol (MCP) extends Thunderflow AI's capabilities by connecting to external tools and services. This guide covers everything you need to know about using MCP with Thunderflow AI.

Configuring MCP Servers

MCP server configurations can be managed at two levels:

1. Global Configuration: Stored in the mcp_settings.json file, accessible via VS Code settings (see below). These settings apply across all your workspaces unless overridden by a project-level configuration.
2. Project-level Configuration: Defined in a .thunderflow/mcp.json file within your project's root directory. This allows you to set up project-specific servers and share configurations with your team by committing the file to version control. Thunderflow AI automatically detects and loads this file if it exists.

Precedence: If a server name exists in both global and project configurations, the project-level configuration takes precedence.

Editing MCP Settings Files

You can edit both global and project-level MCP configuration files directly from the Thunderflow AI MCP settings view:

1. Click the icon in the top navigation of the Thunderflow AI pane.

2. Scroll to the bottom of the MCP settings view.
3. Click the appropriate button:
   • Edit Global MCP: Opens the global mcp_settings.json file.
   • Edit Project MCP: Opens the project-specific .thunderflow/mcp.json file. If this file doesn't exist, Thunderflow AI will create it for you.

Both files use a JSON format with a mcpServers object containing named server configurations:

{
  "mcpServers": {
    "server1": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {
        "API_KEY": "your_api_key"
      },
      "alwaysAllow": ["tool1", "tool2"],
      "disabled": false
    }
  }
}

Example of MCP Server config in Thunderflow AI (STDIO Transport)

Understanding Transport Types

MCP supports two transport types for server communication:

STDIO Transport

Used for local servers running on your machine:

• Communicates via standard input/output streams
• Lower latency (no network overhead)
• Better security (no network exposure)
• Simpler setup (no HTTP server needed)
• Runs as a child process on your machine

For more in-depth information about how STDIO transport works, see STDIO Transport.

STDIO configuration parameters:

• command (required): The executable to run (e.g., node, python, npx, or an absolute path).
• args (optional): An array of string arguments to pass to the command.
• cwd (optional): The working directory from which to launch the server process. If omitted, defaults to the first workspace folder path or the main process's working directory. Useful if the server script relies on relative paths.
• env (optional): An object containing environment variables to set for the server process.
• alwaysAllow (optional): An array of tool names from this server to automatically approve.
• disabled (optional): Set to true to disable this server configuration.

STDIO configuration example:

{
  "mcpServers": {
    "local-server": {
      "command": "node",
      "args": ["server.js"],
      "cwd": "/path/to/project/root", // Optional: Specify working directory
      "env": {
        "API_KEY": "your_api_key"
      },
      "alwaysAllow": ["tool1", "tool2"],
      "disabled": false
    }
  }
}

SSE Transport

Used for remote servers accessed over HTTP/HTTPS:

• Communicates via Server-Sent Events protocol
• Can be hosted on a different machine
• Supports multiple client connections
• Requires network access
• Allows centralized deployment and management

For more in-depth information about how SSE transport works, see SSE Transport.

SSE configuration parameters:

• url (required): The full URL endpoint of the remote MCP server (e.g., https://your-server.com/mcp).
• headers (optional): An object containing custom HTTP headers to send with requests (e.g., for authentication tokens).
• alwaysAllow (optional): An array of tool names from this server to automatically approve.
• disabled (optional): Set to true to disable this server configuration.

SSE configuration example:

{
  "mcpServers": {
    "remote-server": {
      "url": "https://your-server-url.com/mcp",
      "headers": {
        "Authorization": "Bearer your-token" // Example: Authentication header
      },
      "alwaysAllow": ["tool3"],
      "disabled": false
    }
  }
}

Enabling or Disabling MCP Servers

Disabling your MCP Servers here will remove all MCP related logic and definitions from your system prompt, reducing your token usage. This will prevent Thunderflow AI from connecting to any MCP servers, and the use_mcp_tool and access_mcp_resource tools will not be available. Check this off if you don't intend to use MCP Servers. This is on by default.

1. Click the icon in the top navigation of the Thunderflow AI pane
2. Check/Uncheck Enable MCP Servers

Enabling or Disabling MCP Server Creation

Disabling your MCP Server Creation here will just remove the instructions from your system prompt that Thunderflow AI uses to write MCP servers while not removing the context related to operating them. This reduces token usage. This is on by default.

1. Click the icon in the top navigation of the Thunderflow AI pane
2. Check/Uncheck Enable MCP Server Creation

Managing Individual MCP Servers

Each MCP server has its own configuration panel where you can modify settings, manage tools, and control its operation. To access these settings:

1. Click the icon in the top navigation of the Thunderflow AI pane
2. Locate the MCP server you want to manage in the list

Deleting a Server

1. Press the next to the MCP server you would like to delete
2. Press the Delete button on the confirmation box

Restarting a Server

1. Press the button next to the MCP server you would like to restart

Enabling or Disabling a Server

1. Press the toggle switch next to the MCP server to enable/disable it

Network Timeout

To set the maximum time to wait for a response after a tool call to the MCP server:

1. Click the Network Timeout pulldown at the bottom of the individual MCP server's config box and change the time. Default is 1 minute but it can be set between 30 seconds and 5 minutes.

Auto Approve Tools

MCP tool auto-approval works on a per-tool basis and is disabled by default. To configure auto-approval:

1. First enable the global "Use MCP servers" auto-approval option in auto-approving-actions
2. In the MCP server settings, locate the specific tool you want to auto-approve
3. Check the Always allow checkbox next to the tool name

When enabled, Thunderflow AI will automatically approve this specific tool without prompting. Note that the global "Use MCP servers" setting takes precedence - if it's disabled, no MCP tools will be auto-approved.

Finding and Installing MCP Servers

Thunderflow AI does not come with any pre-installed MCP servers. You'll need to find and install them separately.

• Community Repositories: Check for community-maintained lists of MCP servers on GitHub
• Ask Thunderflow: You can ask Thunderflow AI to help you find or even create MCP servers (when "Enable MCP Server Creation" is enabled)
• Build Your Own: Create custom MCP servers using the SDK to extend Thunderflow AI with your own tools

For full SDK documentation, visit the MCP GitHub repository.

Using MCP Tools in Your Workflow

After configuring an MCP server, Thunderflow will automatically detect available tools and resources. To use them:

1. Type your request in the Thunderflow AI chat interface
2. Thunderflow will identify when an MCP tool can help with your task
3. Approve the tool use when prompted (or use auto-approval)

Example: "Analyze the performance of my API" might use an MCP tool that tests API endpoints.

Troubleshooting MCP Servers

Common issues and solutions:

• Server Not Responding: Check if the server process is running and verify network connectivity
• Permission Errors: Ensure proper API keys and credentials are configured in your mcp_settings.json (for global settings) or .thunderflow/mcp.json (for project settings).
• Tool Not Available: Confirm the server is properly implementing the tool and it's not disabled in settings
• Slow Performance: Try adjusting the network timeout value for the specific MCP server

Platform-Specific MCP Configuration Examples

Windows Configuration Example

When setting up MCP servers on Windows, you'll need to use the Windows Command Prompt (cmd) to execute commands. Here's an example of configuring a Puppeteer MCP server on Windows:

{
  "mcpServers": {
    "puppeteer": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "@modelcontextprotocol/server-puppeteer"
      ]
    }
  }
}

This Windows-specific configuration:

• Uses the cmd command to access the Windows Command Prompt
• Uses /c to tell cmd to execute the command and then terminate
• Uses npx to run the package without installing it permanently
• The -y flag automatically answers "yes" to any prompts during installation
• Runs the @modelcontextprotocol/server-puppeteer package which provides browser automation capabilities

macOS and Linux Configuration Example

When setting up MCP servers on macOS or Linux, you can use a simpler configuration since you don't need the Windows Command Prompt. Here's an example of configuring a Puppeteer MCP server on macOS or Linux:

{
  "mcpServers": {
    "puppeteer": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-puppeteer"
      ]
    }
  }
}

This configuration:

• Directly uses npx without needing a shell wrapper
• Uses the -y flag to automatically answer "yes" to any prompts during installation
• Runs the @modelcontextprotocol/server-puppeteer package which provides browser automation capabilities

The same approach can be used for other MCP servers on Windows, adjusting the package name as needed for different server types.

Runtime Version Manager Configuration

When working with multiple versions of programming languages or runtimes, you may use version managers like asdf or mise (formerly rtx). These tools help manage multiple runtime versions on a single system. Here's how to configure MCP servers to work with these version managers:

mise Configuration Example

mise is a fast, modern runtime version manager that can be used to specify which version of Node.js, Python, or other runtimes to use for your MCP server:

{
  "mcpServers": {
    "mcp-batchit": {
      "command": "mise",
      "args": [
        "x",
        "--",
        "node",
        "/Users/myself/workspace/mcp-batchit/build/index.js"
      ],
      "disabled": false,
      "alwaysAllow": [
        "search",
        "batch_execute"
      ]
    }
  }
}

This configuration:

• Uses the mise command to manage runtime versions
• The x subcommand executes a command with the configured runtime version
• The -- separates mise arguments from the command to run
• Runs node with the specific version configured in your mise settings
• Points to the MCP server JavaScript file
• Automatically allows the "search" and "batch_execute" tools

asdf Configuration Example

asdf is a popular tool for managing multiple runtime versions. Here's how to configure an MCP server to use a specific Node.js version managed by asdf:

{
  "mcpServers": {
    "appsignal": {
      "command": "/Users/myself/.asdf/installs/nodejs/22.2.0/bin/node",
      "args": [
        "/Users/myself/Code/Personal/my-mcp/build/index.js"
      ],
      "env": {
        "ASDF_NODE_VERSION": "22.2.0"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

This configuration:

• Directly references the Node.js executable from the asdf installations directory
• Sets the ASDF_NODE_VERSION environment variable to ensure consistent version use
• Points to the MCP server JavaScript file

Using version managers ensures that your MCP servers run with the correct runtime version, regardless of the system's default version, providing consistency across different environments and preventing version conflicts.