Web Search MCP Server with ChromaDB Vector Database
About
Servidor MCP que busca documentação mais atualizada de tools
Details
- Author
- joao-santillo
- Downloads
- 341
- Categories
- Search
Jump to
- Search documentation for LangChain, LlamaIndex, and OpenAI
- Extract content from web pages
- Store documents with vector embeddings via ChromaDB
- Perform semantic similarity search with metadata filtering
- Batch add multiple documents for efficiency
- Create retrievers for downstream AI applications
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Web Search MCP Server with ChromaDB Vector DatabaseCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies with pip install -e . (or uv pip install -e .), create a .env file with a Serper API key and ChromaDB settings, then run python main.py. The server exposes tools for web search and ChromaDB operations, which can be invoked programmatically.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"web search mcp server with chromadb vector database": {
"web-search-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}
}
McpServers
{
"web-search-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
Web Search MCP Server with ChromaDB Vector Database
This MCP server provides tools for web search and vector database functionality using LangChain and ChromaDB.
Features
Web Search
- Search documentation for popular libraries (LangChain, LlamaIndex, OpenAI) - Extract content from web pagesVector Database (ChromaDB)
- Store and retrieve documents with vector embeddings - Perform semantic similarity search - Filter documents based on metadata - Batch operations for efficiencySetup
1. Install dependencies:
```bash
pip install -e .
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