Limitless MCP Server

Overview

The Limitless MCP Integration server is a lightweight, single‑file MCP implementation that bridges an AI assistant with the Limitless Developer API. By exposing the API’s GET endpoint as a formal MCP tool, it removes the need for developers to write custom wrappers or handle authentication details manually. The server accepts a simple query string and returns the raw JSON response from Limitless, allowing AI agents to retrieve lifelog data on demand.

This MCP server solves the common problem of integrating third‑party REST services into conversational AI workflows. Developers often struggle with boilerplate code for authentication, rate limiting, and data transformation. With this server, the entire plumbing is encapsulated behind a clean MCP interface: the tool can be invoked by name, and the assistant only needs to supply optional query parameters in JSON format. The server automatically injects the from the environment, ensuring that credentials never leak into the conversation.

Key capabilities include:

• Tool Exposure: A single, well‑documented tool () that lists lifelog entries with optional filtering parameters such as date ranges or tags.
• Environment‑Aware: Automatic passing of API keys and other secrets to the spawned server process, keeping credentials secure.
• Interactive Client: A command‑line interface that lets developers test tool calls manually, providing immediate feedback on parameter validation and response structure.
• Merged Codebase: The same TypeScript source can be built for both server and client modes, reducing maintenance overhead.

Real‑world use cases span from personal productivity apps that pull user activity logs into a chatbot, to enterprise analytics dashboards where an AI assistant can surface recent events or anomalies. For example, a team could ask the assistant to “show me all lifelog entries from last week tagged with ‘meeting’,” and the MCP server would translate that into a properly authenticated API request, returning the data in JSON for further processing or display.

Integration into existing AI workflows is straightforward. Once deployed, the server registers its tool with any MCP‑compatible assistant (Claude, Gemini, etc.). The assistant’s prompt can reference the tool by name; when invoked, the MCP client handles serialization of arguments and deserialization of responses. Because the server exposes only a single GET endpoint, it imposes minimal latency and reduces attack surface compared to more complex integrations.

What sets this implementation apart is its emphasis on simplicity without sacrificing functionality. By merging server and client logic into one codebase, developers can spin up a fully‑functional MCP bridge with minimal configuration. The modular design also means that adding new Limitless endpoints later is as easy as defining another tool and updating the TypeScript interface—making it a practical starting point for any project that needs to expose external REST APIs to conversational AI.