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Database MCP Provider - Product Requirements Document (PRD)

1. Introduction and Vision

1.1 Problem Statement

Developers and AI agents face significant challenges when working with multiple databases:

Different SQL dialects and connection protocols for each database type
Complex driver management and authentication handling
No unified interface for AI systems to interact with databases
Security concerns with credential management
Difficulty in providing database access to AI assistants safely

1.2 Proposed Solution

Database MCP Server provides a unified MCP tool interface that lets AI agents and developers interact with supported SQL databases using consistent contracts.

1.3 Key Goals

Unified access: one MCP interface for MySQL, MariaDB, PostgreSQL, SQLite
Simplicity: tool-based workflows for configuration, discovery, and querying
Security: encrypted credentials and policy-based read-only profiles
Introspection: programmatic schema and data understanding for agent workflows

2. User Personas

2.1 AI Agent / Orchestrator

Needs: autonomous, consistent database access without custom per-database wiring
Goals: discover schema, generate/validate/optimize SQL, execute safely

2.2 Developer

Needs: one interface across multiple databases inside MCP-capable coding tools
Goals: faster iteration, lower context switching, safer query workflows

3. Product Requirements

3.1 Core Requirements

3.1.1 Multi-Database Support

Requirement: support MySQL, MariaDB, PostgreSQL, SQLite via one MCP interface
Acceptance: consistent tool behavior across supported backends

3.1.2 Connection Profile Management

Interactive first-run profile setup when config is missing
Programmatic profile create/update via configure-profile
Profile inventory via list-profiles

3.1.3 Database Interaction

SQL execution via execute-sql
Read-only enforcement for protected profiles
Schema/database discovery: list-databases, list-tables, describe-table

3.1.4 Intelligence and Governance

Query generation/validation/optimization:
- smart-query-builder
- validate-query
- optimize-query
Analysis and governance:
- analyze-schema
- analyze-data-lineage
- discover-insights
- track-schema-changes
- federated-query

3.2 Security Requirements

AES-GCM encrypted credential storage
No plaintext credential logging
Read-only policy guardrails for exploration use cases
Parameterized statement support

3.3 Performance Requirements

Practical interactive responsiveness for normal analytical workloads
Connection pooling and stateless request handling
Configurable resource constraints (pool size, query limits)

3.4 Integration Requirements

MCP protocol compliance for client interoperability
Primary stdio transport with optional HTTP/SSE deployment path
Tool discovery via list-tools
Structured machine-usable error responses

3.5 Usability Requirements

Fast onboarding (<5 minutes to first successful query in common setups)
Clear examples for profile setup and tool workflows
Troubleshooting guidance for common connection/query failures

4. Success Metrics

Fast time to first successful query
Stable tool discovery and invocation across MCP clients
Zero plaintext credential storage incidents
High documentation discoverability for new users

5. Scope Boundaries

No GUI; MCP and CLI workflows only
No full ORM abstraction layer
Transaction orchestration remains database/client-driven

6. Technical Dependencies

Go runtime baseline: go 1.26 (go1.26.0 toolchain)
Official Go MCP SDK
SQL database drivers for supported backends

7. Future Considerations

Additional database support (e.g., SQL Server, Oracle)
Advanced observability and operational telemetry
Additional enterprise governance workflows
Data Migration (v1.4.0) - Cross-database migration with async jobs, schema translation, and resume capability (see docs/data-migration-design.md)