llm-database-management-tool.ts•8.07 kB
/**
* LLM-Managed Database Management Tool
*
* Provides LLM-driven database operations with research-driven approach
*/
import { McpAdrError } from '../types/index.js';
import type { ToolContext } from '../types/tool-context.js';
import { ResearchOrchestrator } from '../utils/research-orchestrator.js';
/**
* LLM-managed database operations with research-driven approach
*
* @description Executes database operations using LLM to research best practices,
* generate appropriate commands, and execute them. Supports PostgreSQL, MongoDB, Redis, MySQL, and MariaDB.
*
* @param {Object} args - Database management configuration parameters
* @param {string} args.database - Database type (postgresql, mongodb, redis, mysql, mariadb)
* @param {string} args.action - Action to perform
* @param {Object} [args.parameters] - Action parameters
* @param {string} args.llmInstructions - LLM instructions for command generation
* @param {boolean} [args.researchFirst] - Research best approach first (default: true)
* @param {string} [args.projectPath] - Path to project root (defaults to cwd)
* @param {string} [args.adrDirectory] - ADR directory relative to project (defaults to 'docs/adrs')
*
* @returns {Promise<any>} Database operation results with LLM analysis
*
* @throws {McpAdrError} When required parameters are missing or operation fails
*
* @example
* ```typescript
* // PostgreSQL database creation
* const result = await llmDatabaseManagement({
* database: 'postgresql',
* action: 'create_database',
* parameters: { dbName: 'myapp', owner: 'myuser' },
* llmInstructions: 'Create a secure database with proper permissions'
* });
* ```
*
* @example
* ```typescript
* // MongoDB collection optimization
* const result = await llmDatabaseManagement({
* database: 'mongodb',
* action: 'optimize_collection',
* parameters: { collection: 'users', indexFields: ['email', 'created_at'] },
* llmInstructions: 'Optimize for read-heavy workloads with proper indexing'
* });
* ```
*
* @since 2.1.0
* @category Database Management
* @category LLM
* @mcp-tool
*/
export async function llmDatabaseManagement(
args: {
database: 'postgresql' | 'mongodb' | 'redis' | 'mysql' | 'mariadb';
action: string;
parameters?: Record<string, any>;
llmInstructions: string;
researchFirst?: boolean;
projectPath?: string;
adrDirectory?: string;
},
context?: ToolContext
): Promise<any> {
const {
database,
action,
parameters = {},
llmInstructions,
researchFirst = true,
projectPath,
adrDirectory,
} = args;
if (!database || !action || !llmInstructions) {
throw new McpAdrError(
'Database, action, and llmInstructions are required',
'MISSING_REQUIRED_PARAMS'
);
}
try {
context?.info(`🗄️ Initializing ${database} database management for: ${action}`);
context?.report_progress(0, 100);
// Initialize research orchestrator
const orchestrator = new ResearchOrchestrator(projectPath, adrDirectory);
let researchResult = null;
if (researchFirst) {
context?.info('📚 Researching best practices and optimization strategies...');
context?.report_progress(20, 100);
// Step 1: Research the best approach
const researchQuery = `
How to ${action} in ${database}?
Best practices for ${database} ${action}
${database} ${action} documentation and examples
Performance optimization for ${database} ${action}
Security considerations for ${database} ${action}
${llmInstructions}
`;
researchResult = await orchestrator.answerResearchQuestion(researchQuery);
}
// Step 2: Generate command using LLM
context?.info('🤖 Generating database commands with LLM guidance...');
context?.report_progress(50, 100);
const command = await generateDatabaseCommand({
database,
action,
parameters,
research: researchResult,
instructions: llmInstructions,
});
// Step 3: Execute the command (simulated for now)
context?.info(`🗄️ Executing ${database} operation...`);
context?.report_progress(80, 100);
const executionResult = await executeDatabaseCommand(command);
context?.info('✅ Database operation complete!');
context?.report_progress(100, 100);
return {
content: [
{
type: 'text',
text: `# LLM-Managed Database Operation
## Operation Details
- **Database**: ${database}
- **Action**: ${action}
- **Parameters**: ${JSON.stringify(parameters, null, 2)}
## LLM Instructions
${llmInstructions}
${
researchResult
? `
## Research Results
- **Confidence**: ${(researchResult.confidence * 100).toFixed(1)}%
- **Sources**: ${researchResult.metadata.sourcesQueried.join(', ')}
- **Research Summary**: ${researchResult.answer}
`
: ''
}
## Generated Command
\`\`\`sql
${command.generated}
\`\`\`
## Execution Result
${executionResult.success ? '✅ Success' : '❌ Failed'}
${executionResult.output ? `\n\`\`\`\n${executionResult.output}\n\`\`\`` : ''}
## LLM Analysis
${command.analysis || 'No analysis available'}
## Metadata
- **Command Confidence**: ${(command.confidence * 100).toFixed(1)}%
- **Timestamp**: ${new Date().toISOString()}
- **Research-Driven**: ${researchFirst ? 'Yes' : 'No'}
`,
},
],
};
} catch (error) {
throw new McpAdrError(
`Database management operation failed: ${error instanceof Error ? error.message : String(error)}`,
'DATABASE_MANAGEMENT_ERROR'
);
}
}
/**
* Generate database command using LLM
*/
async function generateDatabaseCommand(context: {
database: string;
action: string;
parameters: Record<string, any>;
research: any;
instructions: string;
}): Promise<{ generated: string; confidence: number; analysis: string }> {
// const { loadAIConfig, getAIExecutor } = await import('../config/ai-config.js');
// const aiConfig = loadAIConfig();
// const executor = getAIExecutor();
// const prompt = `
// Generate a ${context.database} command for the following operation:
//
// Action: ${context.action}
// Parameters: ${JSON.stringify(context.parameters, null, 2)}
// Instructions: ${context.instructions}
//
// ${context.research ? `
// Research Context:
// - Confidence: ${(context.research.confidence * 100).toFixed(1)}%
// - Sources: ${context.research.metadata.sourcesQueried.join(', ')}
// - Key Findings: ${context.research.answer}
// ` : ''}
//
// Database Context:
// ${getDatabaseContext(context.database)}
//
// Generate the appropriate command (SQL, CLI, or API call) and provide analysis of the approach.
// `;
// TODO: Implement LLM command generation when AI executor is available
// const result = await executor.executeStructuredPrompt(prompt, {
// type: 'object',
// properties: {
// command: { type: 'string' },
// confidence: { type: 'number' },
// analysis: { type: 'string' }
// }
// });
// return {
// generated: result.data.command || '-- Command generation failed',
// confidence: result.data.confidence || 0.5,
// analysis: result.data.analysis || 'No analysis available'
// };
// Placeholder implementation
return {
generated: `-- LLM command generation for ${context.database} ${context.action} not yet implemented`,
confidence: 0.3,
analysis: 'LLM command generation is not yet available. This is a placeholder implementation.',
};
}
/**
* Execute database command (simulated for now)
*/
async function executeDatabaseCommand(command: {
generated: string;
confidence: number;
}): Promise<{ success: boolean; output: string }> {
// For now, simulate command execution
// In a real implementation, this would execute the actual database command
return {
success: command.confidence > 0.7,
output: `Simulated execution of: ${command.generated}\n\nThis is a simulation. In production, this would execute the actual database command.`,
};
}
// Database context functions will be used when LLM integration is complete