MCP ITSM Integration

# LLM-Enabled Ticket Creation with Model Context Protocol ## Overview This document outlines the integration of Large Language Models (LLMs) and the Model Context Protocol into the ITSM Integration Platform to enable more intelligent, context-aware ticket creation across multiple ITSM systems (ServiceNow, Jira, and Zendesk). ## What is Model Context Protocol? The Model Context Protocol (MCP) is a framework for managing conversations with language models. It provides: 1. **Conversation State Management**: Tracks the state of a conversation between the user and the LLM 2. **Context Awareness**: Ensures the LLM has access to relevant context from previous parts of the conversation 3. **Memory Management**: Maintains important information throughout a conversation 4. **Structured Interaction**: Defines patterns for how the LLM should interact with users and systems ## Integration Architecture Our enhanced architecture integrates the Model Context Protocol with the existing Multi-Channel Platform (the infrastructure foundation) and ITSM Integration Platform. The new LLM processing layer will: 1. Accept natural language inputs from users 2. Process them through an LLM service using appropriate context 3. Extract structured ticket data 4. Route the structured data to the appropriate ITSM system ### Architecture Diagrams For detailed diagrams of this architecture, see: - [MCP-LLM Integration Flow](../diagrams/mcp-llm-integration-flow.mmd) - Overview of the flow between components - [MCP-LLM Component Architecture](../diagrams/mcp-llm-component-architecture.mmd) - Detailed class diagram of the components - [MCP-LLM Sequence Diagram](../diagrams/mcp-llm-sequence.mmd) - Sequence of interactions between components - [MCP-LLM Data Model](../diagrams/mcp-llm-data-model.mmd) - Structure of the data managed by the Model Context Protocol ## Key Components ### 1. LLM Service The central component that interacts with language models (like GPT-4, Claude, or others) to process natural language inputs. This service will: - Interpret user requests - Extract key ticket information - Suggest categories, priorities, and summaries - Generate structured ticket data ### 2. Context Manager Manages conversation context using the Model Context Protocol. This component: - Maintains the state of ongoing conversations - Ensures contextual awareness across multiple messages - Provides relevant historical information to the LLM - Implements the Model Context Protocol interfaces ### 3. Prompt Templates A repository of optimized prompts for different ticket-related tasks: - Ticket classification templates - Priority detection templates - Information extraction templates - User follow-up question templates - Multi-ITSM system templates ### 4. Chat Service Enhancement The existing Chat Service will be enhanced to: - Accept both direct ticket information and LLM-processed inputs - Route LLM-generated ticket data to the appropriate ITSM system - Provide feedback to users on LLM-processed ticket information - Support interactive refinement of tickets ## LLM-Enhanced Ticket Creation Workflow 1. **User Input Phase**: - User describes their issue in natural language - The Chat Client UI captures this input 2. **LLM Processing Phase**: - The natural language input is sent to the LLM Service - The Context Manager retrieves/updates conversation context via Model Context Protocol - The LLM Service processes the input using appropriate prompt templates - Structured ticket data is extracted (summary, description, priority, category) 3. **Verification Phase** (optional): - The extracted ticket information is presented to the user for verification - User can approve or refine the information 4. **Ticket Creation Phase**: - The structured ticket data is sent to the Integration API - The appropriate adapter formats the data for the target ITSM system - The ticket is created in the selected system - Confirmation and ticket details are returned to the user ## Data Model The Model Context Protocol manages several data entities to maintain conversation state: 1. **Conversation**: The top-level entity that represents an ongoing conversation 2. **Message**: Individual messages in the conversation, with role (user/assistant/system) 3. **Metadata**: Additional information about the conversation (selected system, user preferences) 4. **Extracted Data**: Structured information extracted from messages 5. **Entity**: Named entities recognized in messages (people, systems, categories) 6. **Ticket Data**: Structured ticket information ready for submission to ITSM systems ## Benefits of LLM-Enhanced Ticket Creation 1. **Improved User Experience**: - Users can describe issues in natural language - No need to understand specific ITSM system requirements - Reduced form-filling and technical knowledge required 2. **Better Ticket Quality**: - More consistent categorization and prioritization - Improved ticket descriptions through LLM summarization - Automatic extraction of key information 3. **Enhanced Support for Multiple Languages**: - LLMs can translate inputs from different languages - Enables global support without language-specific integrations 4. **Context-Aware Interactions**: - System remembers previous conversations - Can reference previous tickets or related issues - Provides more personalized responses 5. **Efficient Ticket Routing**: - LLM can suggest the most appropriate ITSM system - Can route to specific teams based on content analysis - Improves first-time resolution rates ## Implementation Plan ### Phase 1: Foundation 1. Set up the LLM Service with a selected model provider (OpenAI, Anthropic, etc.) 2. Implement the Model Context Protocol for conversation management 3. Create basic prompt templates for ticket information extraction ### Phase 2: Integration 1. Connect the LLM Service to the existing Chat Client UI 2. Enhance the Chat Service to handle LLM-processed ticket data 3. Implement the verification workflow for user confirmation ### Phase 3: Advanced Features 1. Add multi-turn conversation support for complex tickets 2. Implement ticket suggestion features based on user history 3. Develop automatic categorization and priority assignment 4. Add support for multiple languages ## Technical Requirements ### LLM Provider Requirements - API access to a language model provider - Support for context window of at least 8K tokens - Support for function calling or structured output ### Model Context Protocol Requirements - Persistent storage for conversation state - Efficient context management and pruning - Support for metadata and entity extraction - Secure handling of conversation data ### Frontend Requirements - Support for asynchronous processing of LLM responses - UI components for displaying structured ticket data - Verification interface for user confirmation ## Conclusion Integrating the Model Context Protocol and LLM capabilities into our ITSM Integration Platform will significantly enhance the ticket creation experience. Users will be able to interact in natural language while the system intelligently processes their requests, maintains conversation context, and creates well-structured tickets in the appropriate ITSM system. This enhancement aligns perfectly with our existing architecture, adding an intelligent layer on top of our unified API approach. The result will be a more user-friendly, efficient, and powerful ITSM integration solution.