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semanticintent

Semantic D1 MCP

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suggest_schema_optimizations

Analyze database schema to identify performance improvements like missing or redundant indexes for Cloudflare D1 optimization.

Instructions

Analyze schema and suggest performance optimizations (missing indexes, redundant indexes, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
environmentYesDatabase environment to analyze for optimizations

Implementation Reference

  • Primary handler executing the tool logic: fetches/caches schema, extracts relationships, runs optimization analysis, maps to response.
    async execute(request: SuggestOptimizationsRequest): Promise<OptimizationSuggestionsResponse> {
    	const environment = request.environment;
    
    	// Observable: Cache key based on environment
    	const cacheKey = `schema:${environment}`;
    
    	// Check cache first (avoid repeated API calls)
    	let schema = await this.cache.get<DatabaseSchema>(cacheKey);
    
    	if (!schema) {
    		// Fetch schema from repository
    		const databaseId = this.databaseConfig.getDatabaseId(environment);
    		schema = await this.repository.fetchDatabaseSchema(databaseId);
    
    		// Cache for future requests (10-minute TTL)
    		await this.cache.set(cacheKey, schema, SuggestOptimizationsUseCase.CACHE_TTL_SECONDS);
    	}
    
    	// Extract relationships from schema
    	const relationships = this.relationshipAnalyzer.extractRelationships([...schema.tables]);
    
    	// Get optimization suggestions from domain service
    	const optimizations = this.optimizationService.analyzeSchema([...schema.tables], relationships);
    
    	// Map domain Optimization entities to response DTOs
    	const suggestions = optimizations.map((opt) => this.mapOptimizationToSuggestion(opt));
    
    	return {
    		databaseName: schema.name,
    		environment: schema.environment,
    		optimizationCount: suggestions.length,
    		optimizations: suggestions,
    		analyzedAt: new Date(),
    	};
    }
  • Core analysis method generating optimizations by checking for missing PKs, missing FK indexes, and nullable FKs.
    analyzeSchema(tables: TableInfo[], relationships: Relationship[]): Optimization[] {
      const optimizations: Optimization[] = [];
    
      optimizations.push(...this.checkMissingPrimaryKeys(tables));
      optimizations.push(...this.checkMissingIndexes(tables, relationships));
      optimizations.push(...this.checkNullableForeignKeys(tables));
    
      return optimizations;
    }
  • Tool registration in MCP listTools handler: defines name, description, and JSON input schema.
    {
    	name: 'suggest_schema_optimizations',
    	description:
    		'Analyze schema and suggest performance optimizations (missing indexes, redundant indexes, etc.)',
    	inputSchema: {
    		type: 'object',
    		properties: {
    			environment: {
    				type: 'string',
    				enum: ['development', 'staging', 'production'],
    				description: 'Database environment to analyze for optimizations',
    			},
    		},
    		required: ['environment'],
    	},
    },
  • MCP-specific tool handler: validates args, invokes use case, formats MCP text response.
    private async handleSuggestOptimizations(args: unknown) {
    	const { environment } = args as { environment: string };
    
    	const result = await this.suggestOptimizationsUseCase.execute({
    		environment: parseEnvironment(environment),
    	});
    
    	return {
    		content: [
    			{
    				type: 'text',
    				text: JSON.stringify(result, null, 2),
    			},
    		],
    	};
  • Switch case registration dispatching tool calls to handler.
    case 'suggest_schema_optimizations':
    	return await this.handleSuggestOptimizations(request.params.arguments);
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but provides minimal behavioral details. It mentions analysis and suggestions but doesn't disclose critical traits like whether it's read-only, requires specific permissions, has side effects, or how suggestions are formatted. This is inadequate for a tool with potential operational impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded in a single sentence, with no wasted words. It efficiently conveys the core purpose, though it could be slightly more structured by separating purpose from examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of schema optimization analysis, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents the single parameter (environment). The description adds no additional parameter semantics beyond what the schema provides, such as examples of optimizations or how environment choice affects analysis. Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('analyze') and resource ('schema'), and indicates the outcome ('suggest performance optimizations'). It distinguishes from siblings by focusing on performance rather than relationships or validation, though it doesn't explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling tools (analyze_database_schema, get_table_relationships, validate_database_schema). The description implies usage for performance analysis but lacks explicit context, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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