Semantic D1 MCP

Reference implementation of Semantic Intent as Single Source of Truth patterns

A Model Context Protocol (MCP) server for Cloudflare D1 database introspection, demonstrating semantic anchoring, observable properties, and domain-driven design for AI-assisted database development.

📚 Table of Contents

• What Makes This Different

• Quick Start

• MCP Tools

• Architecture

• Testing

• Contributing

• Security

• License

🎯 What Makes This Different

This isn't just another database introspection tool—it's a reference implementation of proven semantic intent patterns:

• ✅ Semantic Anchoring: Schema analysis based on meaning (table purpose, relationships), not technical metrics (row counts, sizes)

• ✅ Observable Properties: Decisions anchored to directly observable schema markers (foreign keys, indexes, constraints)

• ✅ Intent Preservation: Database semantics maintained through all transformations (development → staging → production)

• ✅ Domain Boundaries: Clear semantic ownership (Schema Domain ≠ Query Optimization Domain ≠ MCP Protocol Domain)

Built on research from Semantic Intent as Single Source of Truth, this implementation demonstrates how to build maintainable, AI-friendly database tools that preserve intent.

🚀 Quick Start

Prerequisites

• Node.js 20.x or higher

• Cloudflare account with D1 databases

• Cloudflare API token with D1 access

Installation

1. Clone the repository

   git clone https://github.com/semanticintent/semantic-d1-mcp.git cd semantic-d1-mcp

2. Install dependencies

   npm install

3. Configure environment

   Copy the example configuration:

   cp .env.example .env

   Update .env with your Cloudflare credentials:

   # Cloudflare Configuration CLOUDFLARE_ACCOUNT_ID=your_cloudflare_account_id CLOUDFLARE_API_TOKEN=your_cloudflare_api_token # D1 Database Configuration - Development D1_DEV_DATABASE_ID=your_dev_database_id D1_DEV_DATABASE_NAME=your_dev_database_name # D1 Database Configuration - Staging (Optional) D1_STAGING_DATABASE_ID=your_staging_database_id D1_STAGING_DATABASE_NAME=your_staging_database_name # D1 Database Configuration - Production (Optional) D1_PROD_DATABASE_ID=your_prod_database_id D1_PROD_DATABASE_NAME=your_prod_database_name

   Note: At least one database environment must be configured.

4. Build the server

   npm run build

5. Start the MCP server

   npm start

   Or use the provided shell script:

   ./start-d1-mcp.sh

Get Cloudflare API Token

1. Go to Cloudflare Dashboard

2. Navigate to My Profile → API Tokens

3. Click Create Token

4. Use the Edit Cloudflare Workers template

5. Add D1 permissions: D1:Read

6. Copy the token to your .env file

Get D1 Database IDs

Copy the database IDs to your .env file.

🛠️ MCP Tools

This server provides 4 comprehensive MCP tools for D1 database introspection:

1. analyze_database_schema

Analyze complete database schema structure with metadata and optional sample data.

Parameters:

• environment (required): "development" | "staging" | "production"

• includeSamples (optional, default: true): Include sample data from tables

• maxSampleRows (optional, default: 5): Maximum rows per table sample

Returns:

• Complete schema analysis

• Table structures with columns, types, constraints

• Indexes and foreign keys

• Sample data from each table (if enabled)

• Schema metadata and statistics

Example:

2. get_table_relationships

Extract and analyze foreign key relationships between tables.

Parameters:

• environment (required): Database environment

• tableName (optional): Filter relationships for specific table

Returns:

• Foreign key relationships with cardinality (one-to-many, many-to-one)

• Referential integrity rules (CASCADE, SET NULL, etc.)

• Relationship metadata and statistics

Example:

3. validate_database_schema

Validate database schema for common issues and anti-patterns.

Parameters:

• environment (required): Database environment

Returns:

• Schema validation results

• Missing primary keys

• Foreign keys without indexes

• Naming convention violations

• Tables without relationships

Example:

4. suggest_database_optimizations

Generate schema optimization recommendations based on structure analysis.

Parameters:

• environment (required): Database environment

Returns:

• Prioritized optimization suggestions (high/medium/low)

• Missing index recommendations

• Primary key suggestions

• Schema improvement opportunities

• Performance optimization tips

Example:

🔌 Connect to Claude Desktop

Connect this MCP server to Claude Desktop for AI-assisted database development.

Configuration

1. Edit Claude Desktop config - Go to Settings → Developer → Edit Config

2. Add MCP server configuration:

3. Restart Claude Desktop

4. Verify tools are available - You should see 4 D1 tools in Claude's tool list

Usage Example

In Claude Desktop:

"Analyze my production database schema and suggest optimizations for tables with foreign keys"

Claude will use the analyze_database_schema and suggest_database_optimizations tools automatically.

🏗️ Architecture

This project demonstrates Domain-Driven Hexagonal Architecture with clean separation of concerns:

Implementation Status

Status: ✅ Hexagonal architecture refactoring complete

Current Structure:

See ARCHITECTURE.md for detailed design documentation.

Layer Responsibilities

Domain Layer:

• Database schema entities (Schema, Table, Relationship, Index)

• Schema analysis business logic

• Relationship extraction logic

• Optimization recommendation rules

Application Layer:

• Orchestrate domain services

• Execute use cases (AnalyzeSchema, GetRelationships, etc.)

• Coordinate infrastructure adapters

Infrastructure Layer:

• Cloudflare D1 REST API integration

• HTTP client for API calls

• Cache provider (in-memory)

Presentation Layer:

• MCP server initialization

• Tool registration and routing

• Request/response formatting

Semantic Intent Principles

This codebase follows strict semantic anchoring rules:

1. Semantic Over Structural

   // ✅ SEMANTIC: Based on observable schema properties const needsIndex = table.hasForeignKey() && !table.hasIndexOnForeignKey() // ❌ STRUCTURAL: Based on technical metrics const needsIndex = table.rowCount > 10000 && table.queryCount > 100

2. Intent Preservation

   // ✅ Environment semantics preserved through transformations const schema = await fetchSchema(Environment.PRODUCTION) // Schema analysis preserves "production" intent - no overrides

3. Observable Anchoring

   // ✅ Based on directly observable properties const relationships = extractForeignKeys(sqliteMaster) // ❌ Based on inferred behavior const relationships = inferFromQueryPatterns(logs)

See SEMANTIC_ANCHORING_GOVERNANCE.md for complete governance rules.

🧪 Testing

Status: ✅ Comprehensive test suite with 398 tests passing

Test Coverage

• ✅ Domain Layer: 212 tests (entities, services, validation)

• ✅ Infrastructure Layer: 64 tests (D1 adapter, API client, config)

• ✅ Application Layer: 35 tests (use cases, orchestration)

• ✅ Presentation Layer: 13 tests (MCP server, tool routing)

• ✅ Integration: 15 tests (end-to-end flows)

• ✅ Value Objects: 59 tests (Environment, immutability)

Total: 398 tests (all passing ✅)

Running Tests

Test Framework

• Vitest: Fast unit testing framework

• @vitest/coverage-v8: Code coverage reports

• Mock Strategy: Mock Cloudflare D1 API responses via interface implementations

📖 Learning from This Implementation

This codebase serves as a reference implementation for semantic intent patterns in database tooling.

Key Files to Study

Hexagonal Architecture Implementation:

• src/index.ts - Composition root with dependency injection

• src/domain/entities/ - Domain entities with semantic validation

• src/domain/services/ - Pure business logic services

• src/application/use-cases/ - Orchestration layer

• src/infrastructure/adapters/ - External adapters

• src/presentation/mcp/ - MCP protocol layer

Reference Documentation:

• D1_MCP_REFACTORING_PLAN.md - Complete refactoring plan

• SEMANTIC_ANCHORING_GOVERNANCE.md - Governance rules

• ARCHITECTURE.md - Architecture details

Related Projects

• semantic-context-mcp - Sibling reference implementation for context management

🤝 Contributing

We welcome contributions! This is a reference implementation, so contributions should maintain semantic intent principles.

How to Contribute

1. Read the guidelines: CONTRIBUTING.md

2. Check refactoring plan: D1_MCP_REFACTORING_PLAN.md

3. Follow the architecture: Maintain layer boundaries and semantic anchoring

4. Add tests: All changes need comprehensive test coverage

5. Document intent: Explain WHY, not just WHAT

Contribution Standards

• ✅ Follow semantic intent patterns

• ✅ Maintain hexagonal architecture (post-refactoring)

• ✅ Add comprehensive tests (90%+ coverage target)

• ✅ Include semantic documentation

• ✅ Pass all CI checks

Quick Links:

• Contributing Guide - Detailed guidelines

• Code of Conduct - Community standards

• Architecture Guide - Design principles

• Security Policy - Report vulnerabilities

Community

• 💬 Discussions - Ask questions

• 🐛 Issues - Report bugs

• 🔒 Security - Report vulnerabilities privately

🔒 Security

Security is a top priority. Please review our Security Policy for:

• API token management best practices

• What to commit / what to exclude

• Reporting security vulnerabilities

• Security checklist for deployment

Found a vulnerability? Email: security@semanticintent.dev

🔬 Research Foundation

This implementation is based on the research paper "Semantic Intent as Single Source of Truth: Immutable Governance for AI-Assisted Development".

Core Principles Applied

1. Semantic Over Structural - Schema analysis based on meaning, not metrics

2. Intent Preservation - Environment semantics maintained through transformations

3. Observable Anchoring - Decisions based on directly observable schema properties

4. Immutable Governance - Protect semantic integrity at runtime

Related Resources

• Research Paper (coming soon)

• Semantic Anchoring Governance

• semanticintent.dev (coming soon)

📊 Project Roadmap

✅ Phase 0: Initial Implementation (Complete)

• Monolithic MCP server with 6 tools

• D1 REST API integration

• Basic schema analysis

✅ Phase 1: Domain Layer (Complete)

• 10 domain entities with semantic validation

• 3 domain services (SchemaAnalyzer, RelationshipAnalyzer, OptimizationService)

• 212 passing tests

✅ Phase 2: Infrastructure Layer (Complete)

• CloudflareD1Repository adapter

• CloudflareAPIClient HTTP client

• InMemoryCacheProvider

• 64 passing tests

✅ Phase 3: Application Layer (Complete)

• 4 use cases (AnalyzeSchema, GetRelationships, ValidateSchema, SuggestOptimizations)

• Port interfaces (ICloudflareD1Repository, ICacheProvider)

• 35 passing tests

✅ Phase 4: Presentation Layer (Complete)

• D1DatabaseMCPServer with 4 MCP tools

• Request/response DTOs

• 13 passing tests

✅ Phase 5: Integration & Composition Root (Complete)

• Dependency injection in index.ts

• Environment configuration

• 15 integration tests

✅ Phase 6: CI/CD & Documentation (Complete)

• TypeScript build verification

• README updated

• 398 total tests passing

🎯 Phase 7: Production Readiness (Planned)

• GitHub Actions CI/CD workflow

• Dependabot automation

• Security scanning

• GitHub repository setup

See D1_MCP_REFACTORING_PLAN.md for detailed roadmap.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on Model Context Protocol by Anthropic

• Inspired by Hexagonal Architecture (Alistair Cockburn)

• Based on Domain-Driven Design principles (Eric Evans)

• Part of the Semantic Intent research initiative

This is a reference implementation demonstrating semantic intent patterns for database introspection. Study the code, learn the patterns, and apply them to your own projects. 🏗️