nanobot
Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps
MCP server by mondaycom · ★ 428 · updated today
Enable AI agents to work reliably - giving them secure access to structured data, tools to take action, and the context needed to make smart decisions.
Choose your AI client and copy the config.
Add to claude_desktop_config.json — macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\. Then restart Claude.
{
"mcpServers": {
"mcp": {
"command": "npx",
"args": [
"@mondaydotcomorg/monday-api-mcp@latest"
]
}
}
}Or add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project).
{
"mcpServers": {
"mcp": {
"command": "npx",
"args": [
"@mondaydotcomorg/monday-api-mcp@latest"
]
}
}
}Or add to .vscode/mcp.json in your workspace.
{
"servers": {
"mcp": {
"type": "stdio",
"command": "npx",
"args": [
"@mondaydotcomorg/monday-api-mcp@latest"
]
}
}
}Run in your terminal:
claude mcp add mcp -- npx @mondaydotcomorg/monday-api-mcp@latestRun the server directly:
npx @mondaydotcomorg/monday-api-mcp@latest"env") in the config.Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
A MCP for Claude Desktop / Claude Code / Windsurf / Cursor to build n8n workflows for you
The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama / Custom Model / All Of The Above] working as one.
Open-source agentic workspace enterprises can make their own. Connect the systems you already run — 100+ integrations, MCP, chat tools, apps, browser, local files — with shared memory. Any agent (Claude Code, Codex), any model, or BYOK. Set up in clicks, not months. Local-first: your data never leaves your machines.
Build effective agents using Model Context Protocol and simple workflow patterns