Toolbox

by ai-zerolab

12 stars

24k downloads

Not rated

GitHub

About

Integrates with external APIs and services to provide command execution, Figma file interaction, and file operations, enhancing LLM capabilities for UI/UX design, file management, and service interactions.

Details

Author: ai-zerolab
Repository: ai-zerolab/mcp-toolbox
GitHub stars: 12
Downloads: 24,027
License: Apache License 2.0
Categories: Developer Tools, AI, Productivity, Design, File Management, Frontend, API, Infrastructure
Tags: #integration

- Command Line Execution: Execute any command line instruction through LLM
- Figma Integration: Access Figma files, components, styles, and more
- Extensible Architecture: Easily add new API integrations
- MCP Protocol Support: Compatible with Claude Desktop and other MCP-enabled LLMs
- Comprehensive Testing: Well-tested codebase with high test coverage

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Toolbox

Command (node, npx, python, etc.) uvx
Arguments
- Argument 1 --prerelease=allow
- Argument 2 mcp-toolbox@latest
- Argument 3 stdio
Environment
- 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
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

JSON

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

From the repository

curl -LsSf https://astral.sh/uv/install.sh | sh # For macOS/Linux

To install Toolbox for LLM Enhancement for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @ai-zerolab/mcp-toolbox --client claude

The following environment variables can be configured:

- FIGMA_API_KEY: API key for Figma integration
- TAVILY_API_KEY: API key for Tavily integration
- DUCKDUCKGO_API_KEY: API key for DuckDuckGo integration
- BFL_API_KEY: API key for Flux image generation API

To use mcp-toolbox with Claude Desktop/Cline/Cursor/..., add the following to your configuration file:

{
  "mcpServers": {
    "zerolab-toolbox": {
      "command": "uvx",
      "args": ["--prerelease=allow", "mcp-toolbox@latest", "stdio"],
      "env": {
        "FIGMA_API_KEY": "your-figma-api-key",
        "TAVILY_API_KEY": "your-tavily-api-key",
        "DUCKDUCKGO_API_KEY": "your-duckduckgo-api-key",
        "BFL_API_KEY": "your-bfl-api-key"
      }
    }
  }
}

For full features:

{
  "mcpServers": {
    "zerolab-toolbox": {
      "command": "uvx",
      "args": [
        "--prerelease=allow",
        "--python=3.12",
        "mcp-toolbox[all]@latest",
        "stdio"
      ],
      "env": {
        "FIGMA_API_KEY": "your-figma-api-key",
        "TAVILY_API_KEY": "your-tavily-api-key",
        "DUCKDUCKGO_API_KEY": "your-duckduckgo-api-key",
        "BFL_API_KEY": "your-bfl-api-key"
      }
    }
  }
}

You can generate a debug configuration template using:

uv run generate_config_template.py

Fork the repository and clone it to your local machine.

bash

make install

source .venv/bin/activate # For macOS/Linux

make test

make check

`execute_command`

Execute a command line instruction.

`read_file_content`

Read content from a file.

`write_file_content`

Write content to a file.

`replace_in_file`

Replace content in a file using regular expressions.

`list_directory`

List directory contents with detailed information.

`figma_get_file`

Get a Figma file by key.

`figma_get_file_nodes`

Get specific nodes from a Figma file.

`figma_get_image`

Get images for nodes in a Figma file.

`figma_get_image_fills`

Get URLs for images used in a Figma file.

`figma_get_comments`

Get comments on a Figma file.

`figma_post_comment`

`figma_delete_comment`

Delete a comment from a Figma file.

`figma_get_team_projects`

Get projects for a team.

`figma_get_project_files`

Get files for a project.

`figma_get_team_components`

Get components for a team.

`figma_get_file_components`

Get components from a file.

`figma_get_component`

Get a component by key.

`figma_get_team_component_sets`

Get component sets for a team.

`figma_get_team_styles`

Get styles for a team.

`figma_get_file_styles`

Get styles from a file.

`figma_get_style`

Get a style by key.

`xiaoyuzhoufm_download`

Download a podcast episode from XiaoyuZhouFM with optional automatic m4a to mp3 conversion.

`get_audio_length`

Get the length of an audio file in seconds.

`get_audio_text`

Get transcribed text from a specific time range in an audio file.

`think`

Use the tool to think about something and append the thought to the log.

`get_session_id`

Get the current session ID.

`remember`

Store a memory (brief and detail) in the memory database.

`recall`

Query memories from the database with semantic search.

`forget`

Clear all memories in the memory database.

`convert_file_to_markdown`

Convert any file to Markdown using MarkItDown.

`convert_url_to_markdown`

Convert a URL to Markdown using MarkItDown.

`get_html`

Get HTML content from a URL.

`save_html`

Save HTML from a URL to a file.

`search_with_tavily`

Search the web using Tavily (requires API key).

`search_with_duckduckgo`

Search the web using DuckDuckGo (requires API key).

`flux_generate_image`

Generate an image using the Flux API and save it to a file.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "toolbox": {
            "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"
            },
            "args": [
                "--prerelease=allow",
                "mcp-toolbox@latest",
                "stdio"
            ],
            "command": "uvx"
        }
    }
}

Linux

{
    "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"
    },
    "args": [
        "--prerelease=allow",
        "mcp-toolbox@latest",
        "stdio"
    ],
    "command": "uvx"
}

Macos

{
    "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"
    },
    "args": [
        "--prerelease=allow",
        "mcp-toolbox@latest",
        "stdio"
    ],
    "command": "uvx"
}

Windows

{
    "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"
    },
    "args": [
        "--prerelease=allow",
        "mcp-toolbox@latest",
        "stdio"
    ],
    "command": "uvx"
}

mcp-toolbox

A comprehensive toolkit for enhancing LLM capabilities through the Model Context Protocol (MCP). This package provides a collection of tools that allow LLMs to interact with external services and APIs, extending their functionality beyond text generation.

- GitHub repository: <https://github.com/ai-zerolab/mcp-toolbox/>
- (WIP)Documentation: <https://ai-zerolab.github.io/mcp-toolbox/>

Features

> \*nix is our main target, but Windows should work too.

Installation

Using uv (Recommended)

We recommend using uv to manage your environment.

```bash

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About

Details

Jump to

Setting up with Highlight

From the repository

execute_command

read_file_content

write_file_content

replace_in_file

list_directory

figma_get_file

figma_get_file_nodes

figma_get_image

figma_get_image_fills

figma_get_comments

figma_post_comment

figma_delete_comment

figma_get_team_projects

figma_get_project_files

figma_get_team_components

figma_get_file_components

figma_get_component

figma_get_team_component_sets

figma_get_team_styles

figma_get_file_styles

figma_get_style

xiaoyuzhoufm_download

get_audio_length

get_audio_text

think

get_session_id

remember

recall

forget

convert_file_to_markdown

convert_url_to_markdown

get_html

save_html

search_with_tavily

search_with_duckduckgo

flux_generate_image

Claude Desktop / Cursor

Linux

Macos

Windows

mcp-toolbox

Features

Installation

Using uv (Recommended)

Install

More like this

Time

Sentry

Github

Git

GitLab

Context 7

`execute_command`

`read_file_content`

`write_file_content`

`replace_in_file`

`list_directory`

`figma_get_file`

`figma_get_file_nodes`

`figma_get_image`

`figma_get_image_fills`

`figma_get_comments`

`figma_post_comment`

`figma_delete_comment`

`figma_get_team_projects`

`figma_get_project_files`

`figma_get_team_components`

`figma_get_file_components`

`figma_get_component`

`figma_get_team_component_sets`

`figma_get_team_styles`

`figma_get_file_styles`

`figma_get_style`

`xiaoyuzhoufm_download`

`get_audio_length`

`get_audio_text`

`think`

`get_session_id`

`remember`

`recall`

`forget`

`convert_file_to_markdown`

`convert_url_to_markdown`

`get_html`

`save_html`

`search_with_tavily`

`search_with_duckduckgo`

`flux_generate_image`