What are the system requirements?
Node.js 22+ is required. You also need a Limitless Pendant (the API only returns Pendant recordings) and a Limitless API key from [limitless.ai/developers](https://limitless.ai/developers).
@ericbuess
基本信息
MCP server for integrating Limitless AI Pendant recordings with AI assistants
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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。
Limitless AI MCP Server is an advanced Model Context Protocol (MCP) server that enables AI assistants to interact with Limitless AI Pendant recordings. It provides a seamless bridge between AI tools and the Limitless API, allowing users to extract insights, search through recordings, and analyze their Pendant data.
First, obtain a Limitless API key from limitless.ai/developers. Then install either globally from npm (npm install -g limitless-ai-mcp-server) or from source. Configure your MCP client (e.g., Claude Desktop or Claude Code CLI) with the server path and API key environment variable. After configuration, you can chat naturally: “Show me my recordings from today.”
Node.js 22+ is required. You also need a Limitless Pendant (the API only returns Pendant recordings) and a Limitless API key from [limitless.ai/developers](https://limitless.ai/developers).
Open your Claude Desktop config file (location varies by OS) and add a `mcpServers` entry with the command `node`, the path to `dist/index.js`, and the `LIMITLESS_API_KEY` environment variable. Restart Claude Desktop for changes to take effect.
Five tools: `limitless_get_lifelog_by_id`, `limitless_list_lifelogs_by_date`, `limitless_list_lifelogs_by_range`, `limitless_list_recent_lifelogs`, and `limitless_search_lifelogs`. Each accepts parameters for filtering, sorting, and content inclusion.
The server uses an LRU (Least Recently Used) eviction strategy with configurable TTL (Time To Live). Separate caches exist for lifelogs (default size: 100, TTL: 5 minutes) and search results (default size: 50, TTL: 3 minutes). Environment variables allow customization.
This is a beta release (v0.0.1). Core features are implemented and tested, but community feedback is needed to validate all functionality. Users should report issues on GitHub.

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