Enables AI agents to remotely access and interact with Obsidian vaults via MCP, supporting note operations, tag management, graph queries, and command execution.
Enables LLM clients to read, search, and write to a local Obsidian vault over MCP, with full-text search, vault analytics, health scoring, and policy-enforced file operations.
Exposes a typed, provenance-aware knowledge graph over an Obsidian vault to coding agents over MCP, so they can trace what grounds a claim, compute which other notes a change makes wrong, surface contradictions and admitted gaps, and assemble a task-specific token-budgeted subset of the vault. Indexing, retrieval, and impact analysis run with zero third-party runtime dependencies beyond the Python standard library.
Turns a plain-Markdown Obsidian vault into a second brain that any MCP-speaking coding agent can search, read, extend, and maintain, using swappable SQLite, Qdrant, or markdown memory backends with automatic re-indexing and wiki-link graph navigation. Exposes read, write, and index tools plus an operator CLI for health, linting, and graph export.