Support Ticket MCP

Overview

Support Ticket MCP is an AI-powered support ticket management system built using FastMCP, Groq LLM, and SQLite.

The assistant can:

• Retrieve ticket details by ID.

• Search all open tickets.

• Search tickets assigned to a specific team.

• Update ticket status.

• Answer users using MCP tools instead of making up information.

Related MCP server: Xalantis MCP Server

Project Structure

support-ticket-mcp/
│
├── agent/
│   ├── main.py
│   ├── client.py
│   └── prompt.py
│
├── mcp_server/
│   ├── server.py
│   ├── tools.py
│   ├── database.py
│   └── resources.py
│
├── db/
│   ├── schema.sql
│   ├── seed.sql
│   ├── create_db.py
│   └── support.db
│
├── requirements.txt
└── README.md

Technologies

• Python

• FastMCP

• Groq API

• SQLite

• python-dotenv

Installation

Clone the repository:

git clone <repository_url>
cd Support-Ticket-MCP

Install dependencies:

pip install -r requirements.txt

Create a .env file:

GROQ_API_KEY=your_api_key

Create Database

python db/create_db.py

Run the Assistant

cd agent
python main.py

Available MCP Tools

get_ticket(ticket_id)

Returns a ticket by its ID.

Example:

Get ticket 1

search_open_tickets()

Returns all open tickets.

Example:

Search open tickets

search_by_team(team_name)

Returns tickets assigned to a team.

Example:

Search Backend tickets

update_ticket_status(ticket_id, status)

Updates a ticket status.

Example:

Update ticket 5 to Closed

Database

The database contains:

• Teams

• Support Tickets

Each ticket includes:

• Customer Name

• Issue

• Category

• Status

• Priority

• Assigned Team

Self-Refine, Reflexion, Grounding, Evaluation & Metrics

This repository now includes the Week 4 Decomposition & Planning lab implementations integrated with the existing planning toolkit. The additions (Self-Refine, Reflexion orchestration, grounding, evaluation harness, and metrics collection) extend the planning layer without replacing the toolkit algorithms already present.

How to run the evaluation harness

1. Ensure dependencies and GROQ_API_KEY are set in .env.

2. Create the database: python db/create_db.py.

3. Run the evaluation harness to execute Plan-and-Solve, Tree-of-Thoughts, and LATS on the fixed reasoning cases:

python -m planning.eval_runner

Artifacts and metrics are written to planning/artifacts/.

Demo and reproducible runs

• Run the MCP server: py -m mcp_server.server (or python -m mcp_server.server)

• Start the agent in another terminal: py -m agent.main

• Provide complex requests (examples in planning/README.md) and observe the integrated planning agent using dynamic decomposition, Self-Refine, Reflexion, and grounded LATS.

Credits

Abdallah Fathi

Final Project — State Graphs

Shared durable state (Member 2 infrastructure): state_graph/common/

Member 1 details: state_graph/README_MEMBER1.md
Member 2 details: state_graph/README_MEMBER2.md
Member 3 / Platform details: state_graph/README_MEMBER3.md and platform/server.py

python demo_member1.py
python -m pytest state_graph/tests/test_sla_breach_escalation.py state_graph/tests/test_sla_breach_restart.py -q
python -m platform_app.server

Platform and agent switching

The repository now includes a local platform server that exposes:

• a user chat surface with selectable agents

• a live admin view for tool toggles and RAG document management

• HITL task review and failure ticket resolution

• a durable state-graph chain for claim appeals and escalations

This adds the missing runtime product surface required for the final project role.