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ai-zerolabMCP SERVER

MCP Toolbox

A general-purpose toolbox in one server — run commands, read and edit files, drive the Figma API, convert anything to Markdown, search the web, generate images, and keep a semantic memory.

by ai-zerolab ↗Design Systems & UI Component LibrariesCommunity

LABSCO SUMMARY

The Figma coverage is deeper than the "toolbox" framing suggests.

Sixteen tools spanning files, nodes, images, comments, components and styles is a real Figma integration, not a token gesture — it is the reason to pick this over assembling three smaller servers. Two things to configure deliberately: execute_command runs shell instructions on your machine, and the audio and memory tools are silently absent until you install the extras.

What it is

A collection of MCP tools grouped by capability rather than by vendor, installed as one Python package. Command execution and file operations sit alongside a full Figma API surface, MarkItDown conversion, web fetching and search, Flux image generation, audio inspection and transcription, and a small memory store with semantic recall. Audio and memory tools ship as optional extras rather than in the default install.

What you get

  • A command line instruction executed on the machine — execute_command
  • Files read, written, edited by regular expression, and directories listed in detail — read_file_content, write_file_content, replace_in_file, list_directory
  • The Figma API covered properly: files and nodes, images and image fills, comments read, posted and deleted, team projects and files, components and component sets, and styles — 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
  • Anything converted to Markdown through MarkItDown, from a file or a URL — convert_file_to_markdown, convert_url_to_markdown
  • Web pages fetched and saved, and search through Tavily or DuckDuckGo — get_html, save_html, search_with_tavily, search_with_duckduckgo
  • Images generated through the Flux API and written to a file — flux_generate_image
  • Audio length and transcribed text from a time range within a file — get_audio_length, get_audio_text
  • A memory store with a thinking log, session identity, and semantic recall — think, get_session_id, remember, recall, forget
  • A podcast episode downloaded from XiaoyuZhouFM with optional m4a to mp3 conversion — xiaoyuzhoufm_download

Requirements

Keys only for the services you use: FIGMA_API_KEY, TAVILY_API_KEY, DUCKDUCKGO_API_KEY and BFL_API_KEY for Flux image generation. The recommended launch is uvx "mcp-toolbox@latest" stdio; audio and memory tools are not in the default install and need the extras — [audio], [memory] or [all], as in uvx "mcp-toolbox[all]@latest" stdio. pip install "mcp-toolbox[all]" works too, after which the command is mcp-toolbox stdio; mcp-toolbox sse --host localhost --port 9871 serves SSE instead. Memory data is stored under ~/Documents/zerolab/mcp-toolbox/memory on macOS, where iCloud syncs it across devices, and ~/.zerolab/mcp-toolbox/memory elsewhere. The README names \*nix as the main target with Windows expected to work.

Setup effort

One command — uvx "mcp-toolbox[all]@latest" stdio