GitHub Stars
106
User Rating
Not Rated
Favorites
0
Views
36
Forks
16
Issues
6
Installation
Difficulty
IntermediateEstimated Time
10-20 minutes
Requirements
Python 3.8以上Claude AI 最新版Installation
Installation
Prerequisites
Please specify required software and versions:Python: 3.8 or higher
Claude AI: Latest version
Installation Steps
1. Clone Repository
bash
git clone https://github.com/AminForou/mcp-gsc.git
cd mcp-gsc
2. Install Dependencies
bash
pip install -r requirements.txt
3. Configure Claude AI
Editclaude_desktop_config.json to add the MCP server:
json
{
"mcpServers": {
"mcp-gsc": {
"command": "python",
"args": ["server.py"]
}
}
}
4. Start Server
bash
python server.py
Troubleshooting
Common Issues
Issue: Server won't start Solution: Check Python version and reinstall dependencies. Issue: Not recognized by Claude AI Solution: Verify configuration file path and syntax.Configuration
Configuration
Basic Configuration
Claude Desktop Setup
Edit~/.config/claude-desktop/claude_desktop_config.json (macOS/Linux) or
%APPDATA%\Claude\claude_desktop_config.json (Windows):
json
{
"mcpServers": {
"mcp-gsc": {
"command": "python",
"args": ["server.py"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Environment Variables
Set the following environment variables as needed:bash
export API_KEY="your-api-key"
export DEBUG="true"
Advanced Configuration
Security Settings
Store API keys in environment variables or secure configuration files
Set appropriate file access permissions
Adjust logging levels
Performance Tuning
Configure timeout values
Limit concurrent executions
Set up caching
Configuration Examples
Basic Configuration
json
{
"mcpServers": {
"mcp-gsc": {
"command": "python",
"args": ["server.py"],
"env": {
"PORT": "3000",
"LOG_LEVEL": "info"
}
}
}
}
Examples
Examples
Basic Usage
Here are basic usage examples for the MCP server:Using with Claude Desktop
1Verify MCP Server Startup
Open Claude Desktop and confirm that the configuration has been loaded correctly.
2Execute Basic Commands
Available tools from this MCP server:
- list_properties: Displays GSC properties
- get_site_details: Shows details about a specific site
Programmatic Usage
python
Python example
import requests
def call_mcp_tool(tool_name, params):
response = requests.post(
'http://localhost:3000/mcp/call',
json={
'tool': tool_name,
'parameters': params
}
)
return response.json()
Usage example
result = call_mcp_tool('get_search_analytics', {
'url': 'your-website-url',
'time_period': 'last_30_days'
})
Use Cases
Analyze search queries for a specific website to devise strategies for increasing traffic.
Check the indexing status of the site and identify pages with issues for correction.
Create a new sitemap and submit it to GSC to promote indexing.
Compare historical performance data to evaluate the effectiveness of SEO strategies.