# Grants MCP Development Roadmap
## Phase 3: Advanced Analytics & Scoring System
### Planned Additional Tools
#### 1. Grant Match Scorer (`grant_match_scorer`)
**Purpose**: Answer "Which grants are actually worth my time?"
**Core Features**:
- **Technical Fit Score** (0-100): Keyword matching, category alignment, institutional focus
- **Competition Index** (NIH/NSF methodology): Application-to-award ratios with historical data
- **ROI Score** (0-100): Award amount vs effort investment analysis
- **Success Probability** (0-100): Based on eligibility matching and past success rates
- **Timing Score** (0-100): Preparation time adequacy assessment
**Mathematical Foundation**:
```python
# Competition Index (based on NIH methodology)
CI = (Total_Applications / Number_of_Awards) * 100
WCI = CI * (1 / sqrt(Award_Ceiling)) * Agency_Weight_Factor
# Success Probability Score
SPS = (Awards / Expected_Applications) * Eligibility_Score * Technical_Fit_Score
# ROI Calculation
ROI = ((Award_Amount - Application_Cost) / Application_Cost) * Success_Probability
```
#### 2. Hidden Opportunity Finder (`hidden_opportunity_finder`)
**Purpose**: Answer "What opportunities am I missing?"
**Discovery Methods**:
- **Under-subscribed Grants**: Identify grants with low application volumes
- **Emerging Funders**: Detect new funding agencies or programs
- **Cross-Category Matching**: Find grants outside typical search categories
- **Geographic Advantages**: Location-based funding preferences
- **Timing Arbitrage**: Off-cycle or unusual deadline opportunities
**Hidden Opportunity Score (HOS)**:
```python
# Novel metric for undersubscribed grants
Visibility_Index = (Search_Result_Position * Category_Popularity) / 100
Undersubscription = max(0, 100 - (Applications_Last_Year / Awards_Available) * 20)
HOS = (Undersubscription * 0.4) + ((100 - Visibility_Index) * 0.3) + (Cross_Category_Score * 0.3)
```
#### 3. Strategic Application Planner (`strategic_application_planner`)
**Purpose**: Answer "How do I maximize my win rate?"
**Planning Features**:
- **Portfolio Diversification**: Reach/match/safety grant categorization
- **Timeline Optimization**: Deadline management and workload balancing
- **Collaboration Suggestions**: Multi-PI or institutional partnerships
- **Resource Allocation**: Budget and time distribution across applications
- **Reuse Opportunities**: Leveraging existing materials and data
**Strategic Metrics**:
- **Portfolio Balance Score**: Ensures diversified risk profile
- **Timeline Feasibility**: Realistic preparation time assessment
- **Collaboration Synergy**: Partnership opportunity scoring
- **Reuse Efficiency**: Material repurposing potential
### Implementation Architecture
```
src/mcp_server/analytics/
├── scoring_engine.py # Main orchestrator for all scoring
├── metrics/
│ ├── competition_metrics.py # CI calculations
│ ├── success_metrics.py # SPS calculations
│ ├── roi_metrics.py # ROI calculations
│ ├── timing_metrics.py # Timing calculations
│ └── hidden_metrics.py # HOS calculations
├── database/
│ ├── session_manager.py # SQLite session persistence
│ ├── score_storage.py # Score caching and storage
│ └── analytics_cache.py # Enhanced caching for calculations
├── transparency/
│ ├── calculation_explainer.py # Transparent score breakdowns
│ └── report_generator.py # Formatted analysis reports
└── utils/
├── numpy_calculator.py # Vectorized mathematical operations
└── industry_constants.py # NIH/NSF benchmark constants
```
### Tools Integration Plan
#### Current Tools (Phase 1-2) ✅
1. `opportunity_discovery` - Basic search and filtering
2. `agency_landscape` - Agency funding pattern analysis
3. `funding_trend_scanner` - Temporal trend analysis
#### Phase 3 Tools (Planned) 🚧
4. `grant_match_scorer` - Multi-dimensional scoring system
5. `hidden_opportunity_finder` - Undersubscribed grant detection
6. `strategic_application_planner` - Portfolio optimization
### Database Schema (Phase 3)
```sql
-- Grant scoring sessions
CREATE TABLE scoring_sessions (
id INTEGER PRIMARY KEY,
user_id TEXT,
search_query TEXT,
created_at TIMESTAMP,
updated_at TIMESTAMP
);
-- Individual grant scores
CREATE TABLE grant_scores (
id INTEGER PRIMARY KEY,
session_id INTEGER,
opportunity_id TEXT,
technical_fit_score REAL,
competition_index REAL,
roi_score REAL,
success_probability REAL,
timing_score REAL,
overall_score REAL,
calculation_details JSON,
FOREIGN KEY (session_id) REFERENCES scoring_sessions(id)
);
-- Strategic planning data
CREATE TABLE application_plans (
id INTEGER PRIMARY KEY,
session_id INTEGER,
grant_ids TEXT, -- JSON array
portfolio_balance REAL,
timeline_feasibility REAL,
total_funding_potential REAL,
recommendation_tier TEXT, -- reach/match/safety
FOREIGN KEY (session_id) REFERENCES scoring_sessions(id)
);
```
### Transparency & Explainability
All scoring calculations will provide detailed breakdowns:
```json
{
"grant_id": "EPA-R13-STAR-G1",
"overall_score": 78.5,
"scores": {
"technical_fit": {
"value": 85.0,
"calculation": "keyword_match(0.9) * category_alignment(0.95) * institutional_focus(0.9)",
"details": {
"keyword_matches": ["renewable", "solar", "energy efficiency"],
"category_alignment": "Environmental Technology - Perfect Match",
"institutional_focus": "R1 Research University - High Priority"
}
},
"competition_index": {
"value": 45.2,
"calculation": "(226 applications / 5 awards) * 100",
"interpretation": "Moderate competition",
"industry_benchmark": "EPA average: 35-45"
},
"roi_score": {
"value": 892.0,
"calculation": "(($500K - $15K) / $15K) * 0.72 probability",
"components": {
"award_amount": 500000,
"application_cost": 15000,
"success_probability": 0.72
}
}
},
"recommendation": "Strong Match - High Priority Application",
"next_actions": [
"Begin preliminary research immediately",
"Contact program officer for guidance",
"Identify potential collaborators"
]
}
```
### Performance Targets (Phase 3)
- **Grant Scoring**: <2 seconds for 10 grants, <10 seconds for 100 grants
- **Hidden Opportunities**: Discover 5-10 overlooked grants per search
- **Strategic Planning**: Generate portfolio recommendations in <5 seconds
- **Database Operations**: <100ms for score retrieval, <500ms for complex analytics
- **Cache Hit Rates**: >80% for repeated calculations
### Success Metrics
**Quantitative Goals**:
- Reduce grant search time by 70%
- Increase application success rate by 2-3x through better targeting
- Identify 15-25% more relevant opportunities via hidden opportunity detection
- Improve portfolio ROI by 40% through strategic planning
**Qualitative Goals**:
- Transparent, explainable scoring that users trust
- Intuitive strategic recommendations
- Seamless integration with existing workflow
- Actionable insights rather than just data
### Technology Stack Additions (Phase 3)
**New Dependencies**:
- `numpy` - Vectorized mathematical operations
- `sqlite3` - Session persistence and analytics storage
- `pandas` (optional) - Data manipulation for complex analytics
- `scikit-learn` (optional) - Advanced matching algorithms
**Enhanced Caching**:
- Persistent SQLite cache for expensive calculations
- NumPy array caching for vectorized operations
- Session-based caching for multi-tool workflows
### Development Priorities
1. **Phase 3.1**: Implement `grant_match_scorer` with basic scoring algorithms
2. **Phase 3.2**: Add `hidden_opportunity_finder` with undersubscription detection
3. **Phase 3.3**: Build `strategic_application_planner` with portfolio optimization
4. **Phase 3.4**: Enhanced analytics, reporting, and visualization capabilities
### Testing Strategy (Phase 3)
**Unit Tests**:
- Individual scoring algorithm accuracy
- Mathematical calculation correctness
- Edge case handling
**Integration Tests**:
- Multi-tool workflow scenarios
- Database persistence and retrieval
- Cache performance under load
**Validation Tests**:
- Scoring accuracy against known successful/unsuccessful applications
- Hidden opportunity detection validation
- Strategic planning recommendation quality
---
This roadmap provides the framework for implementing the remaining analytics tools that will transform the Grants MCP from a search tool into a comprehensive grant strategy platform.