# Success Criteria

## MVP Success Metrics (Phase 1 Complete)

**Operational Excellence**:
- **<100ms semantic search**: Natural language queries return relevant operations within 100ms (p95)
- **<200ms operation execution**: Basic operations complete in <200ms (p95)
- **Zero credential leaks**: 100% automated detection prevents passwords/tokens in logs or errors
- **80%+ test coverage**: All critical paths covered (connection, operations, safety validations)
- **100% MCP compliance**: Passes all protocol specification validation tests

**User Adoption** (Post-Launch, 3 months):
- **50+ GitHub stars**: Developer interest indicator
- **10+ production deployments**: Enterprise adoption validation
- **5+ community contributions**: PRs, issues, documentation improvements
- **90% operation success rate**: Operations complete without errors

**Developer Experience**:
- **<5 minutes first operation**: From `uvx rabbitmq-mcp-server` to executing first operation
- **<30 minutes onboarding**: New developers comfortable with all 3 tools
- **Zero API docs lookups**: Developers rely on semantic search vs external documentation

## Business Objectives

**Primary Objective**: Establish RabbitMQ MCP Server as the **standard tool** for AI-assisted RabbitMQ management in the Python ecosystem.

**Secondary Objectives**:
- Reduce DevOps/SRE time on RabbitMQ operations by 50% (user surveys)
- Demonstrate OpenAPI-driven MCP server pattern for ecosystem
- Build community around AI-assisted infrastructure management
- Enable enterprise adoption with production quality (security, observability, compliance)

**Long-Term Vision** (Phase 2):
- Advanced RabbitMQ features (plugins, clustering, federation)
- Enterprise log aggregation (ELK, Splunk, CloudWatch)
- Multi-language support and internationalization
- Advanced monitoring and performance optimization
- Reference architecture for API-to-MCP transformations

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