home / mcp / zerolab mcp toolbox server
Maintenance of a set of tools to enhance LLM through MCP protocols.
Configuration
View docs{
"mcpServers": {
"ai-zerolab-mcp-toolbox": {
"command": "uvx",
"args": [
"mcp-toolbox[all]@latest",
"stdio"
],
"env": {
"BFL_API_KEY": "YOUR BFL API KEY",
"FIGMA_API_KEY": "YOUR FIGMA API KEY",
"TAVILY_API_KEY": "YOUR TAVILY API KEY",
"DUCKDUCKGO_API_KEY": "YOUR DUCKDUCKGO API KEY"
}
}
}
}You deploy and run an MCP server that lets a large language model interact with external services and APIs through a stable command interface. This enables you to perform tasks like executing system commands, accessing Figma data, reading and writing files, querying the web, generating images, and managing memory, all within an MCP-enabled client. This guide shows practical steps to install, configure, and use the Toolbox MCP server to extend your LLM capabilities.
You run the MCP server locally and connect your MCP-enabled client to it. Start the server using the standard stdio transport to have direct control from your LLM workflow. You can then issue commands, fetch data from Figma, read files, and perform web or media tasks through the model’s prompts.
Prerequisites: you need a modern shell, Node.js for some install options, and access to a package manager or runtimes described below.
# Install uv (recommended) on macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Or install UV via PowerShell on Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"To run the Toolbox MCP server locally with the complete feature set, start the server using stdio transport. The following command uses the all-feature set, including audio and memory tools.
uvx "mcp-toolbox[all]@latest" stdioCreate a configuration that exposes the Toolbox MCP server to your MCP client. You can point the client at a local stdio server or a remote HTTP endpoint if provided.
The Toolbox can be configured with API keys for integrations such as Figma, Tavily, DuckDuckGo, and Flux image generation. These keys are supplied as environment variables in the server configuration.
Example environment-driven startup configuration blocks the required keys to enable full capabilities.
Execute a command line instruction from within the MCP-enabled client.
Read and return the contents of a file.
Write content to a specified file.
Replace content in a file using regular expressions.
List the contents of a directory with detailed information.
Retrieve a Figma file by key.
Fetch specific nodes from a Figma file.
Retrieve images for nodes in a Figma file.
Get URLs for images used in a Figma file.
Get comments on a Figma file.
Post a comment on a Figma file.
Delete a comment from a Figma file.
Get projects for a team in Figma.
Get files for a project in Figma.
Get components for a team.
Get components from a file.
Get a component by key.
Get component sets for a team.
Get styles for a team.
Get styles from a file.
Get a style by key.
Download a XiaoyuZhouFM podcast episode with optional conversion.
Return the duration of an audio file in seconds.
Transcribe audio text for a given time range.
Append a memory-like thought to the log for later recall.
Retrieve the current session ID.
Store a memory in the memory database.
Query memories using semantic search from the memory database.
Clear all memories from the memory database.
Convert a file to Markdown using MarkItDown.
Convert a URL to Markdown using MarkItDown.
Fetch HTML content from a URL.
Save HTML content from a URL to a file.
Search the web using Tavily (requires API key).
Search the web using DuckDuckGo (requires API key).
Generate an image via the Flux API and save it to a file.