Mcp Pkm Logseq

Overview

The mcp‑pkm‑logseq server bridges an AI assistant with a user’s Logseq personal knowledge management (PKM) system. By exposing Logseq content through the Model Context Protocol, it lets Claude or other AI agents query, retrieve, and organize notes in a way that feels natural to the user. The server solves the common pain point of having to manually sift through a growing knowledge graph: it translates human‑readable instructions into precise API calls against Logseq’s HTTP interface, enabling context‑aware assistance without exposing raw data or requiring the user to write code.

At its core, the server offers a set of resources and tools that mirror typical PKM workflows. A resource delivers an introductory instruction set, guiding the AI on how to interact with the user’s graph. The tool explains how to use the personal notes retrieval tool, while pulls blocks tagged with specified topics within a date range. Similarly, fetches the user’s todo list items. These tools are deliberately simple yet powerful: they let developers embed a “search by tag” capability directly into AI prompts, turning the assistant into an on‑demand knowledge explorer.

For developers building conversational agents, this server unlocks several valuable use cases. A research assistant can pull the latest notes on a specific topic to answer questions or summarize trends. A project manager might ask for all completed tasks in a sprint, and the AI will return the relevant Logseq blocks. Because the server uses standard HTTP requests authenticated with an API key, it integrates cleanly into existing AI workflows—whether the agent runs locally on a developer’s machine or in a cloud environment. The server’s configuration is lightweight: only two environment variables ( and ) are required, making deployment straightforward.

Unique to this MCP implementation is the emphasis on a dedicated “MCP PKM Logseq” guide page. Users can document their tagging conventions, naming schemes, and preferred retrieval patterns in a single Logseq page. The server automatically presents this guide whenever the AI needs context, ensuring that the assistant’s behavior aligns with the user’s personal PKM system. This level of customization is rarely seen in generic knowledge‑base connectors and gives developers a powerful way to tailor the AI’s understanding of their data.

In summary, mcp‑pkm‑logseq transforms a Logseq graph from a static repository into an interactive, AI‑driven knowledge source. By providing clear, tag‑based retrieval tools and a built‑in guide mechanism, it empowers developers to create assistants that can search, summarize, and act on personal notes with minimal friction.