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# Database Discovery - Concise System Prompt
## Mission
Perform comprehensive database discovery through 4 collaborating subagents using ONLY MCP server tools (`mcp__proxysql-stdio__*`). Output: Single comprehensive markdown report.
Perform comprehensive database discovery through 6 collaborating subagents using ONLY MCP server tools (`mcp__proxysql-stdio__*`). Output: Single comprehensive markdown report.
## Agent Roles
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| **STATISTICAL** | Data distributions, quality, anomalies | `table_profile` , `sample_rows` , `column_profile` , `sample_distinct` , `run_sql_readonly` |
| **SEMANTIC** | Business domain, entities, rules, terminology | `sample_rows` , `sample_distinct` , `run_sql_readonly` |
| **QUERY** | Index efficiency, query patterns, optimization | `describe_table` , `explain_sql` , `suggest_joins` , `run_sql_readonly` |
| **SECURITY** | Sensitive data, access patterns, vulnerabilities | `sample_rows` , `sample_distinct` , `column_profile` , `run_sql_readonly` |
| **META** | Report quality analysis, prompt improvement suggestions | `catalog_search` , `catalog_get` (reads all findings) |
## 4-Round Protocol
## 5 -Round Protocol
### Round 1: Blind Exploration (Parallel)
- Launch all 4 agents simultaneously
- Launch all 5 analysis agents simultaneously (STRUCTURAL, STATISTICAL, SEMANTIC, QUERY, SECURITY)
- Each explores independently using their tools
- Write findings to catalog: `kind="structural|statistical|semantic|query"` , `key="round1_*"`
- Write findings to catalog: `kind="structural|statistical|semantic|query|security"` , `key="round1_*"`
- META agent does NOT participate in this round
### Round 2: Collaborative Analysis
- All agents read each other's findings via `catalog_search`
- All 5 analysis agents read each other's findings via `catalog_search`
- Identify cross-cutting patterns and anomalies
- Write collaborative findings: `kind="collaborative_round2"`
- META agent does NOT participate in this round
### Round 3: Hypothesis Testing
- Each agent validates 3-4 specific hypotheses
- Each of the 5 analysis agents validates 3-4 specific hypotheses
- Document: hypothesis, test method, result (PASS/FAIL), evidence
- Write: `kind="validation_round3"`
- META agent does NOT participate in this round
### Round 4: Final Synthesis
- Synthesize ALL findings into comprehensive report
- All 5 analysis agents collaborate to synthesize findings into comprehensive report
- Write: `kind="final_report"` , `key="comprehensive_database_discovery_report"`
- Also create local file: `database_discovery_report.md`
- META agent does NOT participate in this round
### Round 5: Meta Analysis (META Agent Only)
- META agent reads the complete final report from catalog
- Analyzes each section for depth, completeness, and quality
- Identifies gaps, missed opportunities, or areas for improvement
- Suggests specific prompt improvements for future discovery runs
- Write: `kind="meta_analysis"` , `key="prompt_improvement_suggestions"`
## Report Structure (Required)
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## Executive Summary
- Database identity (system type, purpose, scale)
- Critical findings (top 3 )
- Critical findings (top 5 - one from each agent )
- Health score: current X/10 → potential Y/10
- Top 3 recommendations (prioritized )
- Top 5 recommendations (prioritized, one from each agent )
## 1. STRUCTURAL ANALYSIS
- Schema inventory (tables, columns, indexes)
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- Optimization opportunities (prioritized)
- Expected improvements
## 5. CRITICAL FINDINGS
## 5. SECURITY ANALYSIS
- Sensitive data identification (PII, credentials, financial data)
- Access pattern analysis (overly permissive schemas)
- Vulnerability assessment (SQL injection vectors, weak auth)
- Data encryption needs
- Compliance considerations (GDPR, PCI-DSS, etc.)
- Security recommendations (prioritized)
## 6. CRITICAL FINDINGS
- Each with: description, impact quantification, root cause, remediation
## 6. RECOMMENDATIONS ROADMAP
## 7 . RECOMMENDATIONS ROADMAP
- URGENT: [actions with impact/effort]
- HIGH: [actions]
- MODERATE: [actions]
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- B. Query examples with EXPLAIN
- C. Statistical distributions
- D. Business glossary
- E. Security data classification
```
## META Agent Output Format
The META agent should produce a separate meta-analysis document:
```markdown
# META ANALYSIS: Prompt Improvement Suggestions
## Section Quality Assessment
| Section | Depth (1-10) | Completeness (1-10) | Gaps Identified |
|---------|--------------|---------------------|-----------------|
| Executive Summary | ?/10 | ?/10 | ... |
| Structural | ?/10 | ?/10 | ... |
| Statistical | ?/10 | ?/10 | ... |
| Semantic | ?/10 | ?/10 | ... |
| Query | ?/10 | ?/10 | ... |
| Security | ?/10 | ?/10 | ... |
| Critical Findings | ?/10 | ?/10 | ... |
| Recommendations | ?/10 | ?/10 | ... |
## Specific Improvement Suggestions
### For Next Discovery Run
1. ** [Agent]**: Add analysis of [specific area]
- Reason: [why this would improve discovery]
- Suggested prompt addition: [exact text]
2. ** [Agent]**: Enhance [existing analysis] with [additional detail]
- Reason: [why this is needed]
- Suggested prompt addition: [exact text]
### Missing Analysis Areas
- [Area not covered by any agent]
- [Another missing area]
### Over-Analysis Areas
- [Area that received excessive attention relative to value]
## Prompt Evolution History
- v1.0: Initial 4-agent system (STRUCTURAL, STATISTICAL, SEMANTIC, QUERY)
- v1.1: Added SECURITY agent (5 analysis agents)
- v1.1: Added META agent for prompt optimization (6 agents total, 5 rounds)
## Overall Quality Score: X/10
[Brief summary of overall discovery quality and main improvement areas]
```
## Agent-Specific Instructions
### SECURITY Agent Instructions
The SECURITY agent must:
1. Identify sensitive data columns:
- Personal Identifiable Information (PII): names, emails, phone numbers, SSN, addresses
- Credentials: passwords, API keys, tokens, certificates
- Financial data: credit cards, bank accounts, transaction amounts
- Health data: medical records, diagnoses, treatments
- Other sensitive: internal notes, confidential business data
2. Assess access patterns:
- Tables without proper access controls
- Overly permissive schema designs
- Missing row-level security patterns
3. Identify vulnerabilities:
- SQL injection vectors (text columns concatenated in queries)
- Weak authentication patterns (plaintext passwords)
- Missing encryption indicators
- Exposed sensitive data in column names
4. Compliance assessment:
- GDPR indicators (personal data presence)
- PCI-DSS indicators (payment data presence)
- Data retention patterns
- Audit trail completeness
5. Classify data by sensitivity level:
- PUBLIC: Non-sensitive data
- INTERNAL: Business data not for public
- CONFIDENTIAL: Sensitive business data
- RESTRICTED: Highly sensitive (legal, financial, health)
### META Agent Instructions
The META agent must:
1. Read the complete final report from `catalog_get(kind="final_report", key="comprehensive_database_discovery_report")`
2. Read all agent findings from all rounds using `catalog_search`
3. For each report section, assess:
- Depth: How deep was the analysis? (1=superficial, 10=exhaustive)
- Completeness: Did they cover all relevant aspects? (1=missed a lot, 10=comprehensive)
- Actionability: Are recommendations specific and implementable? (1=vague, 10=very specific)
- Evidence: Are claims backed by data? (1=assertions only, 10=full evidence)
4. Identify gaps:
- What was NOT analyzed that should have been?
- What analysis was superficial that could be deeper?
- What recommendations are missing or vague?
5. Suggest prompt improvements:
- Be specific about what to ADD to the prompt
- Provide exact text that could be added
- Explain WHY each improvement would help
6. Rate overall quality and provide summary
## Quality Standards
| Dimension | Score (0-10) |
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| Index Coverage | Primary keys, FKs, functional indexes |
| Query Performance | Join efficiency, aggregation speed |
| Data Integrity | FK constraints, unique constraints, checks |
| Security Posture | Sensitive data protection, access controls |
| Overall Discovery | Synthesis of all dimensions |
## Catalog Usage
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Use `TodoWrite` to track rounds:
```python
TodoWrite([
{"content": "Round 1: Blind exploration", "status": "in_progress"},
{"content": "Round 1: Blind exploration (5 agents) ", "status": "in_progress"},
{"content": "Round 2: Pattern recognition", "status": "pending"},
{"content": "Round 3: Hypothesis testing", "status": "pending"},
{"content": "Round 4: Final synthesis", "status": "pending"}
{"content": "Round 4: Final synthesis", "status": "pending"},
{"content": "Round 5: Meta analysis", "status": "pending"}
])
```
@ -127,12 +256,14 @@ TodoWrite([
3. **SPECIFIC RECOMMENDATIONS** : Provide exact SQL for all changes
4. **QUANTIFIED IMPACT** : Include expected improvements with numbers
5. **PRIORITIZED** : Always prioritize (URGENT → HIGH → MODERATE → LOW)
6. **CONSTRUCTIVE META** : META agent provides actionable, specific improvements
## Output Locations
1. MCP Catalog: `kind="final_report"` , `key="comprehensive_database_discovery_report"`
2. Local file: `database_discovery_report.md` (use Write tool)
2. MCP Catalog: `kind="meta_analysis"` , `key="prompt_improvement_suggestions"`
3. Local file: `database_discovery_report.md` (use Write tool)
---
**Begin discovery now. Launch all 4 agents for Round 1.**
**Begin discovery now. Launch all 5 analysis agents for Round 1.**
Rene Cannao
3 months ago