@lishenxydlgzs/simple-files-vectorstore
A Model Context Protocol (MCP) server that provides semantic search capabilities across files. This server watches specified directories and creates vector embeddings of file contents, enabling semantic search across your documents.
Installation & Usage
Add to your MCP settings file:
{
"mcpServers": {
"files-vectorstore": {
"command": "npx",
"args": [
"-y",
"@lishenxydlgzs/simple-files-vectorstore"
],
"env": {
"WATCH_DIRECTORIES": "/path/to/your/directories"
},
"disabled": false,
"autoApprove": []
}
}
}
MCP settings file locations:
- VSCode Cline Extension:
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json - Claude Desktop App:
~/Library/Application Support/Claude/claude_desktop_config.json
Configuration
The server requires configuration through environment variables:
Required Environment Variables
You must specify directories to watch using ONE of the following methods:
WATCH_DIRECTORIES: Comma-separated list of directories to watchWATCH_CONFIG_FILE: Path to a JSON configuration file with awatchListarray
Example using WATCH_DIRECTORIES:
{
"mcpServers": {
"files-vectorstore": {
"command": "npx",
"args": [
"-y",
"@lishenxydlgzs/simple-files-vectorstore"
],
"env": {
"WATCH_DIRECTORIES": "/path/to/dir1,/path/to/dir2"
},
"disabled": false,
"autoApprove": []
}
}
}
Example using WATCH_CONFIG_FILE:
{
"mcpServers": {
"files-vectorstore": {
"command": "npx",
"args": [
"-y",
"@lishenxydlgzs/simple-files-vectorstore"
],
"env": {
"WATCH_CONFIG_FILE": "/path/to/watch-config.json"
},
"disabled": false,
"autoApprove": []
}
}
}
The watch config file should have the following structure:
{
"watchList": [
"/path/to/dir1",
"/path/to/dir2",
"/path/to/specific/file.txt"
]
}
Optional Environment Variables
CHUNK_SIZE: Size of text chunks for processing (default: 1000)CHUNK_OVERLAP: Overlap between chunks (default: 200)IGNORE_FILE: Path to a .gitignore style file to exclude files/directories based on patterns
Example with all optional parameters:
{
"mcpServers": {
"files-vectorstore": {
"command": "npx",
"args": [
"-y",
"@lishenxydlgzs/simple-files-vectorstore"
],
"env": {
"WATCH_DIRECTORIES": "/path/to/dir1,/path/to/dir2",
"CHUNK_SIZE": "2000",
"CHUNK_OVERLAP": "500",
"IGNORE_FILE": "/path/to/.gitignore"
},
"disabled": false,
"autoApprove": []
}
}
}
MCP Tools
This server provides the following MCP tools:
1. search
Perform semantic search across indexed files.
Parameters:
query(required): The search query stringlimit(optional): Maximum number of results to return (default: 5, max: 20)
Example response:
[
{
"content": "matched text content",
"source": "/path/to/file",
"fileType": "markdown",
"score": 0.85
}
]
2. get_stats
Get statistics about indexed files.
Parameters: None
Example response:
{
"totalDocuments": 42,
"watchedDirectories": ["/path/to/docs"],
"processingFiles": []
}
Features
- Real-time file watching and indexing
- Semantic search using vector embeddings
- Support for multiple file types
- Configurable chunk size and overlap
- Background processing of files
- Automatic handling of file changes and deletions
Repository
GitHub Repository
Files Vector Store
Project Details
- lishenxydlgzs/simple-files-vectorstore
- @lishenxydlgzs/simple-files-vectorstore
- MIT License
- Last Updated: 4/17/2025
Categories
Recomended MCP Servers
MCP server enabling Image Generation for LLMs, built in Python and integrated with Together AI.
MCP server to connect to Oracle Database
MCP servers for interacting with Algolia
A powerful Model Context Protocol (MCP) server that helps refine AI-generated content to sound more natural and human-like....
Model Context Protocol (MCP) server for Alpaca trading API
Appwrite’s MCP server. Operating your backend has never been easier.
Talk with Azure using MCP
The core MCP extension for Systemprompt MCP multimodal client





