Simple File Vector Store

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.

{ "mcpServers": { "files-vectorstore": { "command": "npx", "args": [ "-y", "@lishenxydlgzs/simple-files-vectorstore" ], "env": { "WATCH_DIRECTORIES": "/path/to/your/directories" }, "disabled": false, "autoApprove": [] } } }

- 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

The server requires configuration through environment variables:

You must specify directories to watch using ONE of the following methods:

- WATCH_DIRECTORIES: Comma-separated list of directories to watch
- WATCH_CONFIG_FILE: Path to a JSON configuration file with awatchListarray

{ "mcpServers": { "files-vectorstore": { "command": "npx", "args": [ "-y", "@lishenxydlgzs/simple-files-vectorstore" ], "env": { "WATCH_DIRECTORIES": "/path/to/dir1,/path/to/dir2" }, "disabled": false, "autoApprove": [] } } }

{ "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" ] }

- 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

{ "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": [] } } }

This server provides the following MCP tools:

Perform semantic search across indexed files.

- query(required): The search query string
- limit(optional): Maximum number of results to return (default: 5, max: 20)

[ { "content": "matched text content", "source": "/path/to/file", "fileType": "markdown", "score": 0.85 } ]

{ "totalDocuments": 42, "watchedDirectories": ["/path/to/docs"], "processingFiles": [] }

- 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

Search global news using natural language. Webz.io News Search API returns the most relevant articles and content, with filters for source, country, language, date, sentiment, and category.

An MCP server providing semantic search capabilities for APLCart data.

MCP server for Christian scholarship and research — scripture, Greek/Hebrew word data, cross-references, patristic texts, and semantic search,

Access and search EPUB ebook collections using semantic vector search.

local-first semantic search in Lojban dictionaries

Search 419,000+ space regulatory filings from the FCC, ITU, UNOOSA, and FAA-AST — semantic search, entity dossiers, spectrum holdings, launch licenses, and alerts.

Self-hosted web search for AI agents — multi-engine parallel search with embedding-based result reranking. Zero API keys, pip install.

The MCP server provides end-to-end workflows for SEC filings and earnings call transcripts—including ticker resolution, document retrieval, OCR, embedding, on-disk resource discovery, and semantic search—exposed via MCP and powered by the same olmOCR and embedding backends as the vLLM backends.

MCP server that enables AI assistants to search Reddit conversations, explore subreddits, and access trending topics.

Embeddings, vector search, document storage, and full-text search with the open-source AI application database

Semantic search through Dickens' classic tale. Find passages by meaning, theme, or concept - not just keywords.