Obsidian MCP Python

Obsidian MCP Python – Bridging AI Assistants and Personal Knowledge Bases

The Obsidian MCP Python server is a lightweight, ready‑to‑run example that demonstrates how an external service can expose resources and tools to a Model Context Protocol (MCP) client. It solves the practical problem of connecting AI assistants—such as Claude, Cursor, or Windsurf—to a user’s personal knowledge base stored in an Obsidian vault. By exposing the vault as a set of searchable documents, the server allows AI agents to read, query, and manipulate notes in real time without leaving their native interface.

At its core, the server launches a simple Python application that listens for MCP requests. When an AI client sends a prompt, the server can search the vault’s markdown files, retrieve relevant passages, and return them as structured resources. Developers appreciate this because it turns a static file system into an interactive, queryable API that can be consumed by any MCP‑compatible client. The result is a seamless workflow where an AI assistant can ask questions, retrieve context, and even suggest edits or new notes—all through the same conversation interface.

Key features include:

• Vault integration – The server automatically discovers and indexes all markdown files in a specified Obsidian directory, making them available as searchable resources.
• MCP‑compliant endpoints – It follows the MCP specification for resources, tools, and prompts, ensuring compatibility with a wide range of AI platforms.
• Environment‑based configuration – By setting the environment variable, developers can point the server to any vault location without code changes.
• Minimal footprint – Built with and a single Python script, the server can be started with a one‑line command, making it ideal for quick demos or prototyping.

Real‑world scenarios that benefit from this server include:

• Personal knowledge retrieval – An AI assistant can pull the latest meeting notes or research summaries from a user’s vault during a conversation.
• Dynamic note generation – The assistant can suggest new markdown files or update existing ones based on user prompts, effectively acting as a co‑author.
• Workflow automation – Developers can embed the server into larger pipelines, letting AI agents trigger actions in Obsidian (e.g., tagging, linking) as part of a broader automation workflow.

The integration process is straightforward for developers familiar with MCP: they simply add the server’s configuration to their client, specify the command and arguments, and set the vault path. Once running, any MCP‑compatible client can query the server as if it were a native data source. This plug‑and‑play model removes the need for custom adapters or manual data extraction, enabling rapid experimentation and deployment of AI‑powered knowledge management solutions.