Multi-Agent Database Discovery System using Claude Code#12
Multi-Agent Database Discovery System using Claude Code#12renecannao merged 6 commits intov3.1-MCP1_discoveryfrom
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The catalog_search() and catalog_list() methods in MySQL_Catalog.cpp were manually building JSON strings by concatenating raw TEXT from SQLite without proper escaping. This caused parse errors when stored JSON contained quotes, backslashes, or newlines. Changes: - MySQL_Catalog.cpp: Use nlohmann::json to build proper nested JSON in search() and list() methods instead of manual concatenation - MySQL_Tool_Handler.cpp: Add try-catch for JSON parsing in catalog_get() - test_catalog.sh: Fix MCP URL path, add jq extraction for MCP protocol responses, add 3 special character tests (CAT013-CAT015) Test Results: All 15 catalog tests pass, including new tests that verify special characters (quotes, backslashes) are preserved.
Implements a 4-agent collaborative system using Claude Code's Task tool and MCP catalog for comprehensive database analysis: - Structural Agent: Maps tables, relationships, indexes, constraints - Statistical Agent: Profiles data distributions, patterns, anomalies - Semantic Agent: Infers business domain and entity types - Query Agent: Analyzes access patterns and optimization Agents collaborate via MCP catalog across 4 rounds: 1. Blind exploration → 2. Pattern recognition → 3. Hypothesis testing → 4. Final synthesis Includes simple_discovery.py demo and comprehensive documentation.
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Summary of ChangesHello @renecannao, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces a powerful multi-agent system designed to perform in-depth database discovery and analysis. By orchestrating specialized AI agents that communicate and share findings via a Model Context Protocol (MCP) catalog, the system can identify structural details, statistical patterns, semantic meaning, and query optimization opportunities within a database. The changes also include improvements to the underlying MCP catalog's JSON handling capabilities and provide clear documentation and a practical demonstration of the new architecture. Highlights
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Code Review
This pull request introduces a multi-agent database discovery system, including comprehensive documentation and a Python demo. The C++ changes are a significant improvement, replacing manual JSON string construction with the robust nlohmann/json library, which greatly improves correctness and safety, especially when handling special characters. The addition of corresponding test cases in test_catalog.sh is also excellent.
My review includes a few suggestions:
- In the C++ code, I've suggested a minor refactoring to use modern range-based for loops for better readability.
- In the new Python demo script, there's a potential
IndexErrorthat should be addressed. - The architecture diagram in the documentation could be clarified to avoid confusion about the catalog component.
Overall, this is a solid contribution that adds a powerful new capability and improves the existing codebase.
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| print("📊 SYNTHESIZED FINDINGS:") | ||
| print("-" * 60) | ||
| print(f"Table: {structure[0]['document']['table']}") |
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Directly accessing structure[0] here is unsafe and will raise an IndexError if the catalog.get_kind('structure') call returned an empty list. You should add a check to ensure structure and data are not empty before proceeding to access their elements.
| ``` | ||
| ┌─────────────────────────────────────────────────────────────────────┐ | ||
| │ Main Agent (Orchestrator) │ | ||
| │ - Launches 4 specialized subagents in parallel │ | ||
| │ - Coordinates via MCP catalog │ | ||
| │ - Synthesizes final report │ | ||
| └────────────────┬────────────────────────────────────────────────────┘ | ||
| │ | ||
| ┌────────────┼────────────┬────────────┬────────────┐ | ||
| │ │ │ │ │ | ||
| ▼ ▼ ▼ ▼ ▼ | ||
| ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ | ||
| │Struct. │ │Statist.│ │Semantic│ │Query │ │ MCP │ | ||
| │ Agent │ │ Agent │ │ Agent │ │ Agent │ │Catalog │ | ||
| └────────┘ └────────┘ └────────┘ └────────┘ └────────┘ | ||
| │ │ │ │ │ | ||
| └────────────┴────────────┴────────────┴────────────┘ | ||
| │ | ||
| ▼ ▼ | ||
| ┌─────────┐ ┌─────────────┐ | ||
| │ Database│ │ Catalog │ | ||
| │ (testdb)│ │ (Shared Mem)│ | ||
| └─────────┘ └─────────────┘ | ||
| ``` |
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The architecture diagram is a bit confusing regarding the catalog. It shows an 'MCP Catalog' box at the same level as the agents, and also a 'Catalog (Shared Mem)' box at the bottom that the agents interact with. This suggests there might be two different catalogs. Could you clarify if these are the same entity and perhaps simplify the diagram to show a single, shared catalog that all agents communicate with via MCP?
| for (std::vector<SQLite3_row*>::iterator it = resultset->rows.begin(); | ||
| it != resultset->rows.end(); ++it) { |
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For improved readability and to use a more modern C++ idiom, you could replace the traditional iterator-based for loop with a range-based for loop.
for (SQLite3_row* row : resultset->rows) {| for (std::vector<SQLite3_row*>::iterator it = resultset->rows.begin(); | ||
| it != resultset->rows.end(); ++it) { |
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Similar to the search method, this traditional for loop can be simplified by using a more modern C++ range-based for loop. This enhances code readability and maintainability.
for (SQLite3_row* row : resultset->rows) {Implement scripts for running Claude Code in non-interactive mode to perform comprehensive database discovery on any database. Files added: - headless_db_discovery.sh: Bash script implementation - headless_db_discovery.py: Python script implementation (recommended) - HEADLESS_DISCOVERY_README.md: Comprehensive documentation Features: - Works with any database accessible via MCP - Database-agnostic discovery prompt - Comprehensive analysis: structure, data, semantics, performance - Markdown report output with ERD, data quality score, recommendations - CI/CD integration ready - Supports custom MCP server configuration - Configurable timeout, output, verbosity Usage: python scripts/headless_db_discovery.py --database mydb
Move headless database discovery scripts from scripts/ to scripts/mcp/DiscoveryAgent/ClaudeCode_Headless/ for better organization. Also update README to: - Focus only on ProxySQL Query MCP (remove generic database examples) - Use relative paths (./) instead of absolute paths - Simplify configuration documentation Files moved: - scripts/HEADLESS_DISCOVERY_README.md - scripts/headless_db_discovery.py - scripts/headless_db_discovery.sh
…covery - Add DATABASE_DISCOVERY_REPORT.md: Complete multi-agent database discovery findings covering structure, statistics, business domain, and query analysis - Add DATABASE_QUESTION_CAPABILITIES.md: Showcase of 14 question categories answerable via the discovery system with examples - Enhance headless_db_discovery.py: Improve JSON parsing and error handling - Enhance headless_db_discovery.sh: Add better argument handling and validation
Relocate DATABASE_DISCOVERY_REPORT.md and DATABASE_QUESTION_CAPABILITIES.md to scripts/mcp/DiscoveryAgent/ClaudeCode_Headless/examples/ for better organization.
Multi-Agent Database Discovery System
Overview
This PR implements a multi-agent database discovery system using Claude Code's autonomous agent capabilities. The system uses 4 specialized subagents that collaborate via the MCP (Model Context Protocol) catalog to perform comprehensive database analysis.
The Four Discovery Agents
Key Features
Files Added
doc/multi_agent_database_discovery.md- Complete system documentationsimple_discovery.py- Simplified demo of multi-agent patternExample Discovery Output
Database: testdb (E-commerce Order Management)
Critical Findings
True Statistics (After Deduplication)
Related Work
Future Commits
This is the first commit on this branch. Additional commits will follow to:
Note: This PR demonstrates Claude Code's capability to use autonomous subagents for complex database analysis tasks. All findings are stored in the MCP catalog for cross-agent collaboration.