How to Use the Zenserp MCP in Claude Code

Q: What if I need image context while running CI/CD with Zenserp and Claude Code?

You can integrate an initial step that calls searchimages. The resulting data tells your pipeline what visual assets are relevant, which informs the next stages of testing or documentation generation.

Q: How do I check news trends using Zenserp and Claude Code?

A simple command sequence runs searchnews with a target keyword. The output provides titles, snippets, and timestamps. You can then filter this data in your shell script to only alert on articles from the last 24 hours.

Automate deep market intelligence collection for CI/CD pipelines using Claude Code in the terminal.

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Setup Zenserp with Claude Code

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Nightly Market Monitoring Scripts

Write a shell script that runs every night to check competitor activity. Use `search_bing` and pipe the results into a log file. The script can then use `search_news` to append any high-priority articles found, creating a complete daily intelligence report. This is perfect for CI/CD: the pipeline triggers the search tools, writes the raw SERP data, and archives it without needing a GUI or browser.

Automating Global Product Audits

For product teams, you can script an audit that checks pricing across regions. The command sequence executes `search_shopping` for the US market (`search_google`) and then repeats the process using `search_yandex` for European markets. The output is clean JSON—perfect for scripting tools to ingest and compare prices automatically.

Video Content Gap Analysis

When launching a new feature, you need to see if competitors are making videos about it. Run `search_youtube` directly from your script. You can then follow up by running `search_videos` using the same query string to ensure full coverage of visual content. The resulting data proves whether an adequate video presence exists for your product launch, letting you adjust marketing spend before deployment.

Setup guide

Set up Zenserp MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see zenserp-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Zenserp transactions." It will automatically discover and invoke the available Zenserp tools.

Terminal

claude mcp add --transport http zenserp-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Get your connection token →

Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Zenserp MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "zenserp-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Zenserp MCP in Claude Code

You build a simple shell script that calls the MCP server. You can then run this script via GitHub Actions or cron jobs, ensuring that your search results are gathered automatically and consistently every time the pipeline runs.

Yes. You don't need separate scripts for different regions. You can build a loop in your shell script that cycles through `search_google` and `search_yandex`, providing the location parameter each time to cover global gaps.

You can integrate an initial step that calls `search_images`. The resulting data tells your pipeline what visual assets are relevant, which informs the next stages of testing or documentation generation.

A simple command sequence runs `search_news` with a target keyword. The output provides titles, snippets, and timestamps. You can then filter this data in your shell script to only alert on articles from the last 24 hours.

This server touches live search engine results (SERP) and general public search content. The data pulled includes titles, snippets, product prices, and news article metadata.

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