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Web Search MCP Server with ChromaDB Vector Database

@joao-santillo

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基本情報

カテゴリ

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トランスポート

stdio

作者

joao-santillo

Web Search MCP Server with ChromaDB Vector Database について

Servidor MCP que busca documentação mais atualizada de tools

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

Web Search MCP Server with ChromaDB Vector Database の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Web Search MCP Server with ChromaDB Vector Database のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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.

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