some-vault-some-mcp

# some-vault-some-mcp MCP server that gives AI models read/write access to Obsidian vaults with semantic search, link graph analysis, canvas manipulation, and incremental indexing. Built on FastMCP + LanceDB. Embeds notes locally with fastembed (nomic-embed-text-v1.5-Q, 768 dims) by default — no server required. Optionally supports Ollama or OpenAI embeddings. Watches the vault filesystem and re-indexes on change. Warning: This is a hot vibe coded mess, user beware ## What it does - Hybrid search - vector similarity (70%) + full-text keyword (30%), with overlap boost - Semantic search - pure embedding-based retrieval - Exact search - literal substring matching, optional case sensitivity and regex - Note CRUD - create, read, append, prepend, move, delete (soft delete to .trash by default) - Frontmatter parsing - YAML extraction, tag collection (frontmatter + inline #hashtags), property filtering - Wikilink graph - backlinks, outlinks, orphan detection, broken link detection, BFS neighbor traversal (depth 1-5) - Canvas CRUD - create, read, add/update/remove nodes and edges, grid auto-layout, dangling edge cleanup - Daily notes - Moment.js-style date formatting, template support, reads Obsidian's daily-notes config - Incremental indexing - filesystem watcher with 2s debounce, mtime-based change detection - Atomic writes - temp file + POSIX rename, per-path asyncio locks - Tool overrides - rename or disable any tool via YAML config (per-agent customization) ## Requirements - Python 3.11+ - [uv](https://docs.astral.sh/uv/) (for `uvx`) ## Quick start Add this to your MCP client config (Cursor, Windsurf, Claude Code, etc.): ```json { "mcpServers": { "obsidian-vault": { "command": "uvx", "args": ["some-vault-some-mcp", "serve", "--transport", "stdio"], "env": { "VAULT_PATH": "/path/to/your/obsidian/vault" } } } } ``` That's it. `uvx` installs the package from PyPI on first run and keeps it cached. For alternative embe…