🧠 IQRE: Model Context Protocol Service

🌟 Operational Summary

This component is engineered as a strict Model Context Protocol (MCP) server. Its primary function is the immediate analysis and resolution of user support requests. The system decouples business logic from external LLM providers by abstracting AI workflows. Contextual grounding is achieved by consulting Glama.ai, while the generative reasoning engine relies on Cursor AI for crafting authoritative replies.

mermaid graph TD User[External Requester] --> Gateway[HTTP Gateway/API Layer] Gateway --> Processor[Request Pre-processing] Processor --> Orchestrator[Service Orchestration Engine] Orchestrator --> ContextDB[Glama.ai Context Retrieval] Orchestrator --> LLM[Cursor AI Response Synthesis] Orchestrator --> Persistence[Data Abstraction Layer] Persistence --> Store[(SQL Data Repository)]

✨ Key Capabilities

🚀 Rapid Deployment

Prerequisites

• Runtime Environment: Python 3.9 or higher
• Data Store: Operational PostgreSQL instance
• External Services: Configured credentials for Glama.ai and Cursor AI

Setup Procedure

bash

Obtain Source Code

git clone https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server.git cd AI-Customer-Support-Bot--MCP-Server

Environment Activation

python -m venv .venv source .venv/bin/activate # Or .venv\Scripts\activate on Windows

pip install -r requirements.txt

Configuration Initialization

cp .env.example .env

Customize settings within .env (API keys, DB connection strings, etc.)

Configuration Snapshot (Excerpt from .env)

bash

Database Connection

DB_CONNECTION_STRING=postgresql://prod_user:prod_pass@dbhost:5432/enterprise_data

Security Settings

AUTH_SECRET=a-very-long-and-random-key THROTTLE_LIMIT=500 # Requests per minute THROTTLE_WINDOW=60 # Seconds

Launch Sequence

bash

Initialize Schema

createdb enterprise_data

Initiate Service Process

python app.py

System Operational at http://localhost:8080

📡 Interface Specifications

🏗️ System Blueprint

Directory Layout

📦 IQRE-MCP-Service ├── 🏠 main_app.py # Entry point (FastAPI initialization) ├── 🗄️ db_connector.py # Data persistence configuration ├── 🛡️ auth_guard.py # Security middleware and throttling logic ├── 📜 domain_models.py # Pydantic and SQLAlchemy schemas ├── ⚙️ protocol_config.py # MCP specific constants ├── requirements.txt # Package dependencies └── .env.example # Configuration variables template

Architectural Segmentation

🔌 Integrating Novel Intelligence Modules

Onboarding a New LLM Service

1. Install Library: bash pip install

2. Parameter Configuration: bash

Update .env file

NEW_AI_KEY=your-secret-key-here GENERATIVE_AGENT=gemini-2.5-pro

1. Implement Abstraction: Develop a dedicated adapter class inheriting from the core service interface. python

Within the Service Core

class CustomAIAgent: async def formulate_reply(self, prompt: str, history: list) -> str: # API call to the new service SDK return final_output

🔧 Development Lifecycle

Automated Validation

bash pytest tests/ # Execute test suites

Code Style Enforcement

black . # Auto-formatting flake8 . # Linting and style checks mypy . # Static type verification

Containerization

dockerfile

Docker build instructions are forthcoming...

📊 Observability Stack

Operational Telemetry

• Service Heartbeat Status
• Database Connection Health Checks
• Request Volume Metrics
• End-to-End Latency Tracking
• Resource Consumption Monitoring

Structured Logging Format

{ "time": "2024-05-20T15:31:10Z", "severity": "AUDIT", "event": "CONTEXT_UPDATE_SUCCESS", "trace_id": "tx_987654", "detail": "Successfully merged Glama context into prompt." }

🔒 Security Posture

Core Protections

• Access Control: Mandatory token verification for all transactional endpoints.
• Throttling: Defensive rate limiting to prevent resource exhaustion.
• Data Integrity: Rigorous input sanitation to mitigate injection risks (e.g., SQLi).
• Configuration Isolation: Use of environment variables exclusively for secrets.

🚀 Production Readiness

Deployment Configuration

bash

Production Environment Variables

DB_CONNECTION_STRING=postgresql://secure_prod_host/live_data THROTTLE_LIMIT=2000 LOG_LEVEL=ERROR

Scalability Directives

• Implement connection pooling (e.g., PgBouncer) for the PostgreSQL layer.
• Utilize a distributed cache (e.g., Redis) to manage rate limiting state across multiple application instances.
• Deploy behind a robust load balancer (L7) for high availability (HA).
• Integrate performance metrics into Prometheus exporters.

🤝 Contribution Guidelines

We welcome improvements! Follow this workflow for submissions:

Contribution Path

bash

Fork repository and clone locally

Create dedicated branch for your feature/fix

git checkout -b enhancement/new-feature-name

Implement and verify changes

...

Ensure all automated checks pass

pytest

Submit a Pull Request targeting the main development branch

Standards for Acceptance

• Complete unit and integration test coverage for new logic.
• Documentation updates reflecting feature changes.
• Adherence to established coding standards (PEP 8 enforced by tooling).
• Successful CI pipeline execution.

📄 Intellectual Property

This software artifact is governed by the terms of the MIT License (refer to the LICENSE file for specifics).