# Risks and Assumptions

## Key Risks

**Technical Risks**:
1. **MCP Protocol Evolution**: Protocol changes could require server updates
   - *Mitigation*: Follow MCP specification closely, participate in community discussions
   
2. **RabbitMQ API Changes**: Management API breaking changes impact operations
   - *Mitigation*: OpenAPI regeneration pipeline handles updates automatically
   
3. **Performance at Scale**: Semantic search performance with 1000+ operations
   - *Mitigation*: Pre-computed embeddings, efficient querying, tested with full RabbitMQ API

**Adoption Risks**:
4. **AI Assistant Compatibility**: Variations in MCP implementation across AI providers
   - *Mitigation*: Test with multiple clients (Claude, ChatGPT, custom), strict protocol compliance
   
5. **Enterprise Security Requirements**: Stricter authentication/authorization needs
   - *Mitigation*: Phase 2 roadmap includes advanced security (OAuth, RBAC)

6. **Community Engagement**: Open source adoption depends on community contributions
   - *Mitigation*: Comprehensive documentation, contribution guides, responsive maintainers

## Critical Assumptions

**Technical Assumptions**:
- Python 3.12+ available in target environments
- RabbitMQ Management API plugin enabled (standard in most installations)
- AI assistants support MCP protocol stdio transport
- Network connectivity to RabbitMQ Management API (HTTP/HTTPS)

**Market Assumptions**:
- DevOps teams willing to adopt AI-assisted tools for infrastructure management
- MCP protocol gains mainstream adoption among AI providers
- RabbitMQ remains popular message queue solution (current trend continues)
- Enterprise teams prioritize automation and developer productivity

**User Assumptions**:
- Users comfortable with CLI tools and AI assistants
- Natural language operation discovery provides sufficient value over direct API calls
- 80%+ test coverage and structured logging meet enterprise quality expectations

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