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Web Search MCP Server with ChromaDB Vector Database

@joao-santillo

代码仓库

基本信息

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传输方式

stdio

作者

joao-santillo

关于 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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Web Search MCP Server with ChromaDB Vector Database 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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