graph TD
A[User Query] --> B[API Agent - NLP Parser]
B --> C[MCP Server - Tool Router]
C --> D[RAG Engine - Knowledge Retrieval]
C --> E[API Execution Tool]
D --> C
E --> F[External API / System]
F --> G[Response Validation Layer]
G --> H[Final AI Response]

User Input
   ↓
API Agent (Intent Detection)
   ↓
MCP Server (Tool Selection)
   ↓
RAG (Context Injection)
   ↓
API Execution Engine
   ↓
Response Validation
   ↓
Final Result Output

git clone https://github.com/karthikeyanramu/MCP_RAG_AGENT.git
cd MCP_RAG_AGENT

python -m venv venv

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

pip install -r requirements.txt

python server/mcp_server.py

MCP Server running on http://localhost:5000

python -m qa_agent.api_agent_runner

POST http://localhost:5000/execute

{
  "Content-Type": "application/json",
  "Authorization": "Bearer <token-if-needed>"
}

{
  "tool": "api_executor",
  "method": "POST",
  "url": "https://api.example.com/login",
  "headers": {
    "Content-Type": "application/json"
  },
  "body": {
    "username": "test_user",
    "password": "Test@123"
  }
}

{
  "status": 200,
  "message": "Login Successful",
  "token": "eyJhbGciOiJIUzI1NiIs...",
  "validation": "PASSED"
}

graph LR
A[Code Push] --> B[CI Trigger - GitHub Actions]
B --> C[Install Dependencies]
C --> D[Run API Tests via MCP Agent]
D --> E[RAG Validation Layer]
E --> F[Test Report Generation]
F --> G[Deploy / Fail Pipeline]

name: MCP API Tests

on: [push]

jobs:
  test:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: 3.10

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run MCP API Agent
        run: python -m qa_agent.api_agent_runner

netstat -ano | findstr :5000
taskkill /PID <pid> /F

pip install -r requirements.txt