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Web Search MCP Server with ChromaDB Vector Database

@joao-santillo

Repository

Basic information

Category

Search

Transports

stdio

Author

joao-santillo

About Web Search MCP Server with ChromaDB Vector Database

Servidor MCP que busca documentação mais atualizada de tools

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

Web Search MCP Server with ChromaDB Vector Database config

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

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

Web Search MCP Server with ChromaDB Vector Database tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

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

Frequently asked questions

1

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.

2

How are documents persisted?

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

3

What transport modes are supported?

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

4

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`.

5

Can I add multiple documents at once?

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

Comments

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