A comprehensive toolkit for enhancing LLM capabilities through the Model Context Protocol (MCP). This package provides a collection of tools that
MCP Toolbox MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. 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.
Once MCP Toolbox is connected, these are the calls the assistant has available:
execute_command — Execute a command line instructionread_file_content — Read content from a filewrite_file_content — Write content to a filereplace_in_file — Replace content in a file using regular expressionslist_directory — List directory contents with detailed informationfigma_get_file — Get a Figma file by keyfigma_get_file_nodes — Get specific nodes from a Figma filefigma_get_image — Get images for nodes in a Figma filefigma_get_image_fills — Get URLs for images used in a Figma filefigma_get_comments — Get comments on a Figma filefigma_post_comment — Post a comment on a Figma filefigma_delete_comment — Delete a comment from a Figma fileYou will need 4 environment variables: FIGMA_API_KEY, TAVILY_API_KEY, DUCKDUCKGO_API_KEY, BFL_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
@smithery/cli on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. MCP Toolbox's toolset — execute_command, read_file_content, write_file_content and 11 more — is a fair guide to whether it matches your workflow. It is maintained by ai-zerolab; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| 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 | Post a comment on a Figma file |
| 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 |
{
"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"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| FIGMA_API_KEY | Credential the server authenticates with. | Yes |
| TAVILY_API_KEY | Credential the server authenticates with. | Yes |
| DUCKDUCKGO_API_KEY | Credential the server authenticates with. | Yes |
| BFL_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.