Web Search MCP Server with ChromaDB Vector Database

by joao-santillo

341 downloads Not rated yet
GitHub

About

Servidor MCP que busca documentação mais atualizada de tools

Explore

- 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:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Web Search MCP Server with ChromaDB Vector Database
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

1. Install dependencies:

pip install -e .

CHROMA_PERSIST_DIRECTORY=./chroma_db
EMBEDDING_MODEL_NAME=sentence-transformers/all-MiniLM-L6-v2

python

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",
            "."
        ]
    }
}

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 pages

Vector Database (ChromaDB)

- Store and retrieve documents with vector embeddings - Perform semantic similarity search - Filter documents based on metadata - Batch operations for efficiency

Setup

1. Install dependencies:
```bash
pip install -e .

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