Web Search MCP Server with ChromaDB Vector Database

Name: Web Search MCP Server with ChromaDB Vector Database
Author: joao-santillo

@joao-santillo

Repository

About Web Search MCP Server with ChromaDB Vector Database

Servidor MCP que busca documentação mais atualizada de tools

#web-search #vector-database #documentation-tools

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "web-search-mcp-server": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Web Search MCP Server with ChromaDB Vector Database?

This MCP server provides tools for web search and vector database functionality using LangChain and ChromaDB. It enables searching documentation for popular libraries (LangChain, LlamaIndex, OpenAI) and storing/retrieving documents via vector embeddings with semantic similarity search.

How to use Web Search MCP Server with ChromaDB Vector Database?

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.

Key features of Web Search MCP Server with ChromaDB Vector Database

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

Use cases of Web Search MCP Server with ChromaDB Vector Database

Searching official documentation for library-specific queries
Building a semantic search over a custom document collection
Adding, querying, updating, and deleting documents programmatically
Creating a retriever to feed relevant context to language models

FAQ from Web Search MCP Server with ChromaDB Vector Database

What dependencies does this server require?

Python packages listed in the project plus a Serper API key for web search and a sentence‑transformer embedding model.

How are documents persisted?

ChromaDB persists data to a directory specified by the CHROMA_PERSIST_DIRECTORY environment variable (default ./chroma_db).

What transport modes are supported?

The server supports both stdio and sse transport modes, configured via the TRANSPORT environment variable.

What is the Serper API used for?

The Serper API performs web searches; it is required and configured with SERPER_API_KEY and SERPER_API_URL.

Can I add multiple documents at once?

Yes, the batch_add_documents_to_vectordb tool accepts a list of documents for efficient batch insertion.

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