FastMCP Todo Server MCP Server
@DanEdens
基本信息
关于 FastMCP Todo Server MCP Server
A comprehensive MCP-based todo management system, that serves as a central nervous system for Madness Interactive, a multi-project task coordination workshop.
FastMCP Todo Server MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"fastmcp-todo-server": {
"command": "python",
"args": [
"-m",
"src.Omnispindle.stdio_server"
]
}
}
}FastMCP Todo Server MCP Server 工具
工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is FastMCP Todo Server?
FastMCP Todo Server is a Python FastMCP server providing 38 tools for AI agents to manage tasks, capture knowledge, coordinate sessions, track epic goals (quests), and access project context through a single standardized interface. It integrates with Auth0 for authentication and can run in API, hybrid, local, or auto operation modes. Designed for multi-project AI-assisted development labs.
How to use FastMCP Todo Server?
Install via pip install omnispindle. Run the stdio server (omnispindle-stdio) for Claude Desktop or the HTTP web server (omnispindle/omnispindle-server) for authenticated endpoints. Set environment variables like OMNISPINDLE_MODE, OMNISPINDLE_TOOL_LOADOUT, and MCP_USER_EMAIL. For Claude Desktop, add a JSON entry to claude_desktop_config.json with the command omnispindle-stdio and required environment variables.
Key features of FastMCP Todo Server
- Todo management with full metadata, priority, target agent, and change detection
- Knowledge capture with language, topic, tags, and semantic vector search
- Session tracking with genealogy trees, forking, and spawning
- Quest system for multi-step objectives with progress reports
- Context bundles giving agents a full project picture in one call
- Zero‑config Auth0 device flow authentication
- Tool loadouts (full, basic, minimal, etc.) to control tool availability
- Operation modes: API, hybrid, local, auto
Use cases of FastMCP Todo Server
- AI agents creating, updating, and completing tasks across multiple projects
- Capturing lessons learned and retrieving them via text or semantic similarity
- Coordinating AI work
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