Seo Data Api Mcp Server

MCP Server

Installation

Prerequisites

Before you begin, please ensure you have the following software and accounts ready:

• SE Ranking Account: You will need an active SE Ranking account to generate an API token. If you don’t have one, you can sign up here.
• Docker: A platform for developing, shipping, and running applications in containers. If you don’t have it, you can download it from the official Docker website.
• Docker Compose Plugin: The Docker Compose plugin is a Docker CLI plugin that provides a command-line interface (CLI) for Docker Compose. To build the Docker image, you need to have Docker Compose installed.
• Git: A free and open-source distributed version control system. You can download it from the official Git website.
• AI Assistant: You will need either the Claude Desktop App or the Gemini CLI installed and configured on your machine.

Installation Steps

Follow these steps to install the SEO-MCP server on your local machine.

• Open your terminal (or Command Prompt/PowerShell on Windows).
• Clone the project repository from GitHub. This command downloads the necessary files to your machine.

git clone https://github.com/seranking/seo-data-api-mcp-server.git

• Navigate into the new directory in your terminal:

cd seo-data-api-mcp-server

Build the Docker Image

Make sure you're in the seo-data-api-mcp-server repository directory, and run:

docker compose build
# Check that the image is built and named `se-ranking/seo-data-api-mcp-server`:
docker image ls

Note: don't worry about the warnings, this is normal when building the image:

WARN[0000] The "SERANKING_API_TOKEN" variable is not set. Defaulting to a blank string.

How to Update SEO-MCP

To ensure you have the latest features and improvements, you should update the tool periodically.

• Navigate to your seo-data-api-mcp-server directory in the terminal.
• Pull the latest changes from the GitHub repository.

git pull origin main

Connect to Claude Desktop

Claude Desktop reads its configuration from claude_desktop_config.json.

• Click on the Claude menu and select Settings....
• In the Settings window, navigate to the Developer tab in the left sidebar.
• Click the Edit Config button to open the configuration file. This action creates a new configuration file if one doesn’t exist or opens your existing configuration.

The file is located at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %AppData%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Example of Claude Desktop configuration for MCP server

JSON Configuration Template:

{
  "mcpServers": {
    "seo-data-api-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERANKING_API_TOKEN",
        "se-ranking/seo-data-api-mcp-server"
      ],
      "env": {
        "SERANKING_API_TOKEN": ""
      }
    }
  }
}

• You need to change the SERANKINGAPITOKEN, get yours from https://online.seranking.com/admin.api.dashboard.html

• After saving claudedesktopconfig.json, restart Claude Desktop. You should see the server under MCP Servers/Tools.

• To verify the setup, ask Claude: Do you have access to MCP? It should respond by listing seo-data-api-mcp.

• Your setup is complete! You can now run complex SEO queries using natural language.

Connect to Gemini CLI

• Open the Gemini CLI settings file, which is typically located at: ~/.gemini/settings.json
• Add the following JSON configuration, making sure to replace the SERANKINGAPITOKEN placeholder value.

{
  "mcpServers": {
    "seo-data-api-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERANKING_API_TOKEN",
        "se-ranking/seo-data-api-mcp-server"
      ],
      "env": {
        "SERANKING_API_TOKEN": ""
      }
    }
  }
}

"SERANKING_API_TOKEN": Use your personal API token, which you can generate from the SE Ranking API Dashboard.

• Save the configuration file.

• To verify the setup, launch the Gemini CLI by running gemini in your terminal. Once the interface is active, press Ctrl+T to view the available MCP servers. Ensure seo-data-api-mcp is listed.

• Your setup is complete! You can now run complex SEO queries using natural language.

Run as HTTP Server (Node)

In order to run the local Node server, you need to have Node.js 20+ version installed on your machine.

Run the following commands in your terminal:

npm install

To build the project, use the following command:

npm run build

To start the server use the command:

npm run start-http

Then your HTTP server should be running at: http://0.0.0.0:5000/mcp.

In case you'd like to modify the HOST and PORT, you can do so by creating a .env file in the root directory of the project with the settings you want to override, for example:

HOST=127.0.0.1
PORT=5555

Additionally, when you're running the server in the external environments/tools like Replit and similar, you can set the SERANKING_API_TOKEN environment variable in the configuration panel, and the application will use it when you start the server.

Note: If you change the SERANKING_API_TOKEN value when the server is running, you need to restart the server.

Usage Example: Finding Keyword Opportunities

With the configuration complete, you can now use natural language prompts to interact with your SE Ranking data. Here is an example prompt to identify low-hanging keyword opportunities for a domain.

Copy and paste the following into your configured AI assistant:

Use the seo-mcp to identify the Keywords my domain is overlooking and find low-hanging fruit opportunities.
1. Analyze my domain's keyword performance:
   - Find keywords my domain has lost (not ranking) using the tool for domainKeywords with pos_change=lost.
   - Find keywords where my domain's position has gone down using the tool for domainKeywords with pos_change=down.
2. Conduct a competitive analysis:
   - Identify my top 2 competitors by finding all competitors with the tool for domainCompetitors and ordering them by common_keywords DESC.
   - Find 30 keywords that these competitors are ranking for but my domain is not. Use the domainKeywordsComparison tool with diff=1, order_field=volume, and order_type=DESC.
3. Identify new keyword opportunities:
   - For 10 of the competitor keywords found in the previous step, use the tools for relatedKeywords and similarKeywords to find the top 5 related and similar keywords for each, ordered by volume DESC.
4. Synthesize and Report:
   - Create a final report of the findings. In the report, highlight potential low-hanging fruit from the new keyword opportunities by analyzing their CPC and keyword difficulty.
Domain to review: seranking.com
Market: us

What This Prompt Does

This prompt instructs the model to perform a comprehensive competitive analysis by:

• Finding Lost & Declining Keywords: It first identifies keywords for seranking.com that have either been lost from the rankings or have dropped in position.
• Identifying Top Competitors: It finds all organic competitors and sorts them to identify the top two based on the number of shared keywords.
• Uncovering Competitor-Ranked Keywords: It compares seranking.com against its top competitors to find 30 high-volume keywords that they rank for, but seranking.com does not.
• Generating a Final Report: Finally, it synthesizes all of this information into a concise report, highlighting the most promising opportunities for growth.

To send the sample test request, which will verify if your server runs properly and has the correct setup, open another terminal window, and run the following command with your SE Ranking API token provided as an argument:

./test-http-server-curl-request.sh ''

For batch MCP Requests testing, you can use this script:

./test-batch-http-server-curl-request.sh ''

Troubleshooting

Docker image problems

If you’re having trouble getting the MCP server to connect, here are a few steps to diagnose the issue.

Verifying if the Docker container is running when you run your AI Assistant (Claude or Gemini). If you start a chat with an AI, and your MCP Server is set up properly via JSON config, it should automatically start the Docker container.

To check if it’s running, open your terminal and run:

docker ps

If it’s working correctly, you will see an output similar to this:

CONTAINER ID   IMAGE                                    COMMAND                  CREATED          STATUS          PORTS     NAMES
de4e410ef0fd   se-ranking/seo-data-api-mcp-server       "docker-entrypoint.s…"   9 seconds ago    Up 8 seconds              musing_bhabha

This confirms that your AI assistant has successfully started the container, and it’s listening for connections.

If docker ps shows no running containers related to the MCP server, it usually points to a configuration issue:

• Incorrect/invalid JSON: Double-check the claudedesktopconfig.json or settings.json for any syntax errors like missing commas or brackets. You can validate the JSON here: https://jsonlint.com/.

• Incorrect image name: Double-check the claudedesktopconfig.json or settings.json for the correct image name se-ranking/seo-data-api-mcp-server

Docker image running, but MCP server is not visible in Claude Desktop / Gemini CLI

If the docker image is running, but the MCP server is not visible in Claude Desktop, you can investigate the container by:

docker inspect 

For the following example, the container ID is de4e410ef0fd:

CONTAINER ID   IMAGE                                    COMMAND                  CREATED          STATUS          PORTS     NAMES
de4e410ef0fd   se-ranking/seo-data-api-mcp-server       "docker-entrypoint.s…"   9 seconds ago    Up 8 seconds              musing_bhabha

Note: you can shorten the container ID in case no other containers share that prefix, ex. docker inspect de4. When you're running multiple AI chats and/or tools connected to the MCP Servers, there can be multiple containers running, but all with the same IMAGE name.

This will show you the big JSON output, where particularly important is the "Config" section, ex.:

{
  "Config": {
    "Hostname": "0977c3dc06fb",
    "Domainname": "",
    "User": "",
    "AttachStdin": true,
    "AttachStdout": true,
    "AttachStderr": true,
    "Tty": false,
    "OpenStdin": true,
    "StdinOnce": true,
    "Env": [
      "SERANKING_API_TOKEN=8abcdef-6fdd-a981-3ad5-123456",
      "PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
      "NODE_VERSION=20.19.5",
      "YARN_VERSION=1.22.22",
      "NODE_ENV=production"
    ]
  }
}

Which shows you if you have the correct environment variables set.

Support

SE Ranking API documentation: https://seranking.com/api/integrations/mcp/.

In case you have any questions or need help, please contact us at [api@seranking.com](mailto:api@seranking.com)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Teake1404
• Source: Teake1404/seo-data-api-mcp-server
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.