Where does Knowledge_vault store data?
Data is stored in the user’s home directory at `~/.knowledge-vault/knowledge.jsonl` in JSON Lines format.
@fak111
Basic information
A local knowledge management MCP server built with FastMCP framework.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"knowledge-vault": {
"command": "uv",
"args": [
"--directory",
"/path/to/your/local_store_mcp",
"run",
"python",
"start_server_new.py"
],
"cwd": "/path/to/your/local_store_mcp",
"description": "Local Knowledge Management MCP Server"
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Knowledge_vault is a local knowledge management MCP server built with the FastMCP framework and Python. It enables users to store, search, and manage knowledge records directly on their machine, with features like automatic title generation, tag suggestion, and fuzzy search. It is intended for developers and power users who want a private, file-based knowledge base integrated with MCP-compatible clients (e.g., Claude Desktop).
After installing dependencies with uv, add the server configuration to your Claude Desktop config file (~/.config/claude_desktop_config.json) using the provided JSON snippet, then restart Claude Desktop. Use natural language commands like “Store a programming tip: …” or “Search for Python knowledge” to interact with the server.
Data is stored in the user’s home directory at `~/.knowledge-vault/knowledge.jsonl` in JSON Lines format.
Use `uv` to sync dependencies, then add the server configuration to `~/.config/claude_desktop_config.json` as shown in the README, and restart Claude Desktop.
It provides seven tools: `store_knowledge`, `search_knowledge`, `list_recent`, `get_knowledge`, `suggest_tags`, `search_by_tags`, and `get_stats`.
Yes, the server is thread safe and uses file locking for concurrent access.
Python 3.10 or newer, along with the `uv` package manager for dependency management.

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