# Product Context: SkyFi MCP
## Why This Project Exists
The proliferation of autonomous AI systems across industries has created a critical need for seamless access to high-quality geospatial data. Current solutions fail to provide the comprehensive integration required for AI agents to efficiently interact with geospatial platforms.
SkyFi MCP addresses this gap by offering a fully-featured platform that allows AI agents to perform complex geospatial tasks autonomously, positioning SkyFi as the default choice for AI-driven geospatial data needs.
## Problems It Solves
### For AI Developers
- **Integration Complexity**: Simplifies complex API integrations into conversational interfaces
- **Documentation Gaps**: Provides comprehensive, framework-specific documentation
- **Authentication Friction**: Streamlines authentication flows
- **Time to Integration**: Reduces integration time from hours to under 30 minutes
### For Enterprise Customers
- **Scalability**: Provides enterprise-grade, scalable infrastructure
- **Reliability**: Offers 99.9% uptime SLA with automated failover
- **Multi-user Support**: Enables secure multi-user credential management
- **Monitoring**: Automated monitoring and alerting capabilities
### For Research Institutions
- **Data Discovery**: Advanced tools for exploring available geospatial data
- **Metadata Access**: Comprehensive metadata for research use cases
- **Pricing Transparency**: Clear, research-friendly pricing structures
- **Export Capabilities**: Tools for data export and analysis
### For End Users
- **Intuitive Interfaces**: Simple interfaces for complex AI interactions
- **Price Clarity**: Clear pricing information before order placement
- **Order Visibility**: Full order tracking and status updates
- **Self-Service**: Complete orders without technical assistance
## How It Should Work
### Core User Flows
#### 1. Developer Integration Flow
```
Discover → Review Docs → Get Credentials → Deploy Server → Test → Integrate
```
- Developer discovers SkyFi MCP through documentation or community
- Reviews framework-specific integration guides
- Obtains API credentials from SkyFi
- Deploys MCP server (local Docker or cloud)
- Tests with sample queries
- Integrates with their AI agent
#### 2. Data Discovery & Ordering Flow
```
Search → Refine → Review → Check Pricing → Confirm → Order → Monitor
```
- AI agent initiates data search with natural language
- Iteratively refines search criteria based on results
- Reviews available data options with metadata
- Checks pricing and feasibility
- Confirms order details
- Places order with price confirmation
- Monitors order status
#### 3. Monitoring Setup Flow
```
Define AOI → Configure Parameters → Setup Webhook → Activate → Receive Updates
```
- User defines Area of Interest (AOI) programmatically
- Configures monitoring parameters (frequency, triggers)
- Sets up webhook endpoints for notifications
- Activates monitoring
- Receives real-time notifications for data updates
## User Experience Goals
### Primary Goals
1. **Simplicity**: Integration should feel effortless, not overwhelming
2. **Speed**: First successful integration in under 30 minutes
3. **Clarity**: All interfaces self-explanatory with minimal learning curve
4. **Reliability**: Consistent, predictable behavior
5. **Feedback**: Clear status updates and confirmations at every step
### Interface Principles
- **Clarity**: Self-explanatory interfaces
- **Consistency**: Follow established patterns and conventions
- **Feedback**: Provide clear status updates and confirmations
- **Error Handling**: Graceful error messages with actionable guidance
- **Documentation**: Contextual help and examples readily available
### Accessibility
- WCAG 2.1 AA compliance for all interfaces
- Well-structured, machine-readable API responses
- Accessible documentation formats (Markdown, HTML)
- Keyboard navigation support where applicable
## Key Value Propositions
### For SkyFi
- **Market Expansion**: Access to growing AI agent market
- **Sales Growth**: 20% increase through AI-driven channels
- **Brand Positioning**: Become the default geospatial data provider for AI
- **Developer Community**: Build engaged developer ecosystem
### For Developers
- **Time Savings**: Reduce integration time significantly
- **Framework Support**: Native support for preferred frameworks
- **Comprehensive Docs**: Everything needed in one place
- **Open Source Demo**: Learn from working examples
### For End Users
- **Automation**: Enable AI agents to handle geospatial tasks
- **Cost Efficiency**: Clear pricing before commitment
- **Reliability**: Enterprise-grade infrastructure
- **Flexibility**: Local or cloud deployment options
## Success Indicators
### Adoption Metrics
- Number of active AI agent integrations
- Developer registrations and API usage
- Time to first successful integration
- Community engagement (GitHub stars, downloads)
### Quality Metrics
- API response times (< 500ms target)
- Uptime percentage (99.9% target)
- Error rates and resolution times
- Developer satisfaction (NPS)
### Business Metrics
- Sales volume from AI-driven orders
- User base growth (15% target)
- Search engine rankings for AI-related terms
- Community ratings (4.5+ stars target)
---
**Last Updated**: January 2025
