Naver MCP Server Integration

What does “Naver” MCP Server do?

The Naver MCP Server is a Model Context Protocol (MCP) server designed to integrate Naver’s suite of OpenAPI services into AI development workflows. By acting as a bridge between AI assistants and Naver’s external data sources, it empowers developers to build assistants capable of real-time blog, news, book, encyclopedia, image, and local information searches, among others. This server exposes a range of read and action tools to query, check, and retrieve diverse content from Naver, allowing for advanced contextual data fetching, sophisticated prompt workflows, and powerful automation scenarios. With easy environment-based API credential management and modular deployment options, the Naver MCP Server simplifies the process of harnessing Naver’s extensive content ecosystem within modern AI applications.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

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List of Resources

No explicit MCP resources are listed in the repository or documentation.

List of Tools

• Blog Search: Search for blog posts on Naver using keywords.
• News Search: Retrieve news articles relevant to a search term.
• Book Search: Find books and get advanced book details.
• Advanced Book Search: Get detailed book information by title or ISBN.
• Adult Content Check: Determine if a search term is related to adult content.
• Encyclopedia Search: Search through Naver’s encyclopedia entries.
• Cafe Article Search: Look up articles in Naver cafes.
• Q&A Search: Search Naver’s Q&A platform for questions and answers.
• Local Search: (Mentioned, but parameters not detailed)
• Spelling Correction: Correct spelling errors in given text.
• Web Search: Search the web via Naver.
• Image Search: Search for images with filtering options.
• Shopping Search: Find shopping items with filters.
• Document Search: Search documents in Naver’s ecosystem.

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Use Cases of this MCP Server

• Content Aggregation for AI Assistants: Enable conversational agents to fetch and summarize blog, news, encyclopedia, or Q&A content on demand, providing more contextually aware responses.
• Market and Book Research: Allow applications to search for books or shopping items, retrieve advanced book details, or compare products directly within development tools.
• Content Moderation and Filtering: Automate adult content checks and spelling correction to improve the quality and safety of user-submitted queries and generated content.
• Local and Community Insights: Retrieve local information or Naver cafe articles for location-based recommendations or community-driven Q&A.
• Image and Multimedia Search: Enhance creative workflows by allowing LLMs to search for relevant images or documents using Naver’s media APIs.

How to set it up

Windsurf

No Windsurf-specific instructions provided.

Claude

1. Install the package in Claude Desktop:

   pip install mcp-naver
   

2. Run the MCP server with API credentials:

   python -m mcp-naver.hosts.claude_desktop \
     -e NAVER_CLIENT_ID=<YOUR NAVER CLIENT ID> \
     -e NAVER_CLIENT_SECRET=<YOUR NAVER CLIENT SECRET>
   

3. Edit your Claude configuration file to add the MCP server.
4. Example JSON configuration:

   {
     "mcpServers": {
       "naver-mcp": {
         "command": "python",
         "args": [
           "-m", "mcp-naver.hosts.claude_desktop"
         ],
         "env": {
           "NAVER_CLIENT_ID": "<YOUR NAVER CLIENT ID>",
           "NAVER_CLIENT_SECRET": "<YOUR NAVER CLIENT SECRET>"
         }
       }
     }
   }
   

5. Save the config and restart Claude Desktop. Verify the MCP server is running.

Securing API Keys

Store API keys using environment variables in the configuration:

"env": {
  "NAVER_CLIENT_ID": "<YOUR NAVER CLIENT ID>",
  "NAVER_CLIENT_SECRET": "<YOUR NAVER CLIENT SECRET>"
}

Cursor

1. Install the package in Cursor:

   pip install mcp-naver
   

2. Run the MCP server with API credentials:

   python -m mcp-naver.hosts.cursor \
     -e NAVER_CLIENT_ID=<YOUR NAVER CLIENT ID> \
     -e NAVER_CLIENT_SECRET=<YOUR NAVER CLIENT SECRET>
   

3. Edit your Cursor configuration to add the MCP server.
4. Example JSON configuration:

   {
     "mcpServers": {
       "naver-mcp": {
         "command": "python",
         "args": [
           "-m", "mcp-naver.hosts.cursor"
         ],
         "env": {
           "NAVER_CLIENT_ID": "<YOUR NAVER CLIENT ID>",
           "NAVER_CLIENT_SECRET": "<YOUR NAVER CLIENT SECRET>"
         }
       }
     }
   }
   

5. Save and restart Cursor. Confirm that the server is accessible.

Securing API Keys

Use env in your configuration to securely store Naver API credentials.

Cline

No Cline-specific instructions provided.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "naver-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “naver-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Naver MCP Server provides a broad suite of actionable tools for leveraging Naver’s APIs and has clear, practical setup documentation for Claude and Cursor. However, it lacks explicit MCP prompt/resource definitions and deeper integration/sampling/roots documentation, making it less feature-complete for advanced MCP use cases. Overall, it is a solid and practical implementation for Naver API access in AI workflows, but not fully utilizing all MCP protocol primitives.

Rating: 6/10

MCP Score