Local LLM Obsidian Knowledge Base Server

Overview

The Local LLM Obsidian Knowledge Base MCP server bridges the gap between a locally hosted large language model (LLM) and a user‑managed knowledge base stored in an Obsidian vault. By exposing the vault’s file system and content through a standard MCP interface, it allows AI assistants—such as Claude or other LLM clients—to query, retrieve, and even update notes in real time. This solves the common developer pain point of keeping an AI’s knowledge current with a private, ever‑evolving codebase or documentation set, without exposing that data to external services.

At its core, the server offers a lightweight development container that bundles an LLM runtime (e.g., GPT‑4o or a custom open‑source model) alongside the Obsidian vault. Developers can clone the template, add their own repository via or , and use an MCP client like VS Code’s Cline extension to establish a bidirectional connection. The server then serves the vault as an MCP resource, enabling prompt construction that references specific markdown files or metadata tags. The LLM can generate answers based on the latest content, and even suggest edits that are written back to the vault through the same protocol.

Key capabilities include:

• Real‑time knowledge lookup: The LLM can fetch the most recent version of a note or a set of notes matching search criteria.
• Contextual prompt building: Clients can inject relevant snippets into prompts, ensuring that the assistant’s responses are grounded in the latest local data.
• Write‑back support: Generated summaries, code snippets, or documentation updates can be committed directly to the vault via MCP commands.
• Sandboxed execution: Running the LLM locally keeps sensitive data on premises, addressing compliance and privacy concerns.

Typical use cases span from internal technical support bots that answer questions about a company’s codebase, to personal knowledge‑management assistants that help writers draft articles by pulling in relevant research notes. In a development workflow, the server can be invoked as part of CI pipelines to auto‑generate documentation from source comments, or during pair programming sessions where the assistant pulls in related design patterns stored in the vault.

What sets this MCP server apart is its seamless integration with Obsidian’s powerful graph and tag features, combined with a minimal dev‑container setup that abstracts away the complexities of model deployment. Developers who are already comfortable with MCP and Obsidian gain a powerful, privacy‑preserving AI companion that stays in sync with their evolving knowledge base.