Obsidian Index MCP Server

The Obsidian Index server turns a local Obsidian vault into an AI‑ready knowledge base. By exposing recently modified notes as resources and providing a semantic search tool, it allows Claude or any MCP‑compatible assistant to query the vault’s content as if it were a dynamic knowledge graph. This solves the common developer pain point of “how do I let an AI read and search my personal notes?” without needing to export or manually ingest data.

The server’s core value lies in its two complementary capabilities. First, it publishes each note as a resource using the scheme, making markdown files instantly discoverable by the client. Second, it offers a tool that performs vector‑based semantic search over the indexed text. The combination lets an assistant retrieve contextually relevant passages, summarize sections, or suggest related notes—all while keeping the data local and private.

Key features include:

• Real‑time indexing – a flag keeps the internal vector store in sync with file changes, so new notes or edits are immediately searchable.
• Multi‑vault support – multiple vault paths can be supplied, enabling a single server to serve several knowledge domains.
• Lightweight deployment – the server runs as a simple Python process, requiring only a local SQLite database for persistence.
• Secure access – resources are addressed by custom URLs that the MCP client can resolve without exposing file paths or credentials.

Typical use cases involve developers who maintain extensive technical documentation in Obsidian and want an AI assistant to answer questions, generate summaries, or guide debugging sessions by pulling directly from their notes. For example, a DevOps engineer can ask Claude to “find the last configuration change for service X” and receive the exact markdown snippet, or a product manager can request “list all user feedback points from last sprint” and get a curated list. In research settings, the tool can surface relevant literature notes or project logs during brainstorming.

Integrating this server into an AI workflow is straightforward: once the MCP client registers the server, it can call with a query string. The client receives a list of matching resources and can request the full markdown content via the resource URL, enabling rich contextual responses. The server’s design ensures minimal latency and preserves privacy by keeping all data on the local machine, making it a standout solution for teams that value both agility and security.