Gitingest-MCP

Name: Gitingest-MCP
Availability: InStock
Author: Purav

mcp server for gitingest

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117

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Installation

Difficulty

Intermediate

Estimated Time

10-20 minutes

Requirements

Node.js: 18.0.0以上

npm: 8.0.0以上

+1 more

Installation

Prerequisites

Please specify required software and versions:

Node.js: 18.0.0 or higher

npm: 8.0.0 or higher

Claude Desktop: Latest version

Installation Steps

1. Install via Smithery

bash
npx -y @smithery/cli@latest install @puravparab/gitingest-mcp --client claude --config "{}" # Claude
npx -y @smithery/cli@latest run @puravparab/gitingest-mcp --client cursor --config "{}" # Cursor

2. Install via GitHub

1Add this to the MCP client config file:

json
{
  "mcpServers": {
    "gitingest-mcp": {
      "command": "/uvx",
      "args": [
        "--from",
        "git+https://github.com/puravparab/gitingest-mcp",
        "gitingest-mcp"
      ]
    }
  }
}

3. Installing Repo Manually

1Clone the repo:

bash
git clone https://github.com/puravparab/Gitingest-MCP
cd Gitingest-MCP

2Install dependencies:

bash
uv sync

3Add this to the MCP client config file:

json
{
  "mcpServers": {
    "gitingest": {
      "command": "/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with-editable",
        "/gitingest_mcp",
        "mcp",
        "run",
        "/gitingest-mcp/src/gitingest_mcp/server.py"
      ]
    }
  }
}

4. Troubleshooting

If you encounter issues, refer to the [MCP server documentation](https://modelcontextprotocol.io/quickstart/server).

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": {
    "gitingest-mcp": {
      "command": "/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with-editable",
        "/gitingest_mcp",
        "mcp",
        "run",
        "/gitingest-mcp/src/gitingest_mcp/server.py"
      ]
    }
  }
}

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": {
    "gitingest-mcp": {
      "command": "/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with-editable",
        "/gitingest_mcp",
        "mcp",
        "run",
        "/gitingest-mcp/src/gitingest_mcp/server.py"
      ]
    }
  }
}

Advanced Configuration

json
{
  "mcpServers": {
    "gitingest-mcp": {
      "command": "python",
      "args": ["-m", "server"],
      "cwd": "/path/to/server",
      "env": {
        "PYTHONPATH": "/path/to/modules",
        "CONFIG_FILE": "/path/to/config.json"
      }
    }
  }
}

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:
   - tool1: Description of tool1
   - tool2: Description of tool2

Programmatic Usage

javascript
// JavaScript example (Node.js)
const { MCPClient } = require('@modelcontextprotocol/client');

const client = new MCPClient();
await client.connect();

// Execute tool
const result = await client.callTool('toolName', {
  parameter1: 'value1',
  parameter2: 'value2'
});

console.log(result);

Advanced Examples

Automation Script

bash
#!/bin/bash
Batch processing example

for file in *.txt; do
  mcp-tool process "$file"
done

API Integration

python
Python example
import requests
import json

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('analyze', {
    'input': 'sample data',
    'options': {'format': 'json'}
})