🚀 MCP RAG Agent – AI-Powered API Testing Framework

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📌 Overview

The MCP RAG Agent is an AI-driven modular testing framework that combines:

• 🔎 RAG (Retrieval Augmented Generation) – Knowledge-based context retrieval
• ⚙️ MCP Layer (Tool Execution Engine) – Executes tools dynamically
• 🧪 API Testing Agent – Automates API validation like Postman

It enables natural language → API execution → validation → intelligent response generation.

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🧠 System Architecture

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]

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🧩 Architecture Explanation

1️⃣ API Agent Layer

• Accepts natural language input
• Converts request into structured API test case

2️⃣ MCP Server Layer

• Central orchestration layer
• Routes requests to appropriate tools

3️⃣ RAG Layer

• Fetches contextual knowledge from documents
• Enhances API validation logic

4️⃣ Execution Layer

• Executes API calls (GET/POST/PUT/DELETE)
• Captures response payloads

5️⃣ Validation Layer

• Compares expected vs actual response
• Returns structured test result

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🔁 End-to-End Flow

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

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⚙️ Installation Guide

1️⃣ Clone Repository

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

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2️⃣ Create Virtual Environment

python -m venv venv

Activate:

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Start MCP Server

python server/mcp_server.py

Expected:

MCP Server running on http://localhost:5000

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5️⃣ Run API Agent

python -m qa_agent.api_agent_runner

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🧪 Postman Integration (Manual Testing Support)

Even though this system is AI-driven, it supports Postman-style API testing.

📌 Example Request

🔹 Endpoint

POST http://localhost:5000/execute

🔹 Headers

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

🔹 Sample Payload

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

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📌 Sample Response

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

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🔄 CI/CD Pipeline (QA Maturity Model)

This system can be integrated into CI/CD pipelines for automated API validation.

🚀 Pipeline Flow

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]

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🧪 CI/CD Benefits

✔ Automated API regression testing ✔ AI-driven validation (reduces manual QA effort) ✔ Early defect detection ✔ Domain knowledge injection via RAG ✔ Scalable test execution

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📌 Sample GitHub Actions Workflow

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

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🧰 Available Tools

Tool	Purpose
knowledge_search	RAG-based document retrieval
calculator	Arithmetic operations
api_executor	Executes HTTP requests

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📊 Real-World Use Cases

• Banking API automation (AML / KYC)
• Collateral management system testing
• Microservices regression testing
• AI-driven QA automation frameworks

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⚠️ Troubleshooting

❌ Port conflict

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

❌ Module error

pip install -r requirements.txt

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🚀 Future Enhancements

• OpenAI / LLM integration
• UI dashboard for test execution
• Kubernetes deployment
• Advanced embedding-based RAG
• Postman collection auto-import

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👨‍💻 Summary

This project demonstrates:

✔ AI-powered API testing ✔ MCP-based tool orchestration ✔ RAG-enhanced validation ✔ Enterprise-grade QA automation architecture