SQLew▌
by sin5ddd
SQLew boosts multi-agent coordination with efficient SQLite design, cutting context sharing tokens by 96% for decision a
Optimizes multi-agent coordination through intelligent SQLite database design with normalized tables, integer enums, and pre-aggregated views to achieve 96% token reduction in context sharing for decision tracking, agent messaging, file change monitoring, and constraint management.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / AI coding assistants that need persistent memory
- / Multi-agent systems requiring coordination
- / Development teams tracking architectural decisions
- / Projects with complex constraint management
capabilities
- / Store and version architectural decisions with metadata
- / Track file modifications and database operations
- / Define and manage project constraints with priorities
- / Manage tasks with kanban workflow and file tracking
- / Query past decisions to avoid repeating debates
- / Suggest related decisions based on context patterns
what it does
Provides AI agents with persistent memory by storing architectural decisions, constraints, and task management in SQLite databases to eliminate repeated context and maintain consistency across sessions.
about
SQLew is a community-built MCP server published by sin5ddd that provides AI assistants with tools and capabilities via the Model Context Protocol. SQLew boosts multi-agent coordination with efficient SQLite design, cutting context sharing tokens by 96% for decision a It is categorized under ai ml, databases. This server exposes 8 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install SQLew in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
Apache-2.0
SQLew is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
sqlew

Design decisions, remembered by SQL — an MCP server for AI agents
What is sqlew?
The Problem
Every AI coding session starts from scratch. Your agent doesn't remember that you chose PostgreSQL over MongoDB last week, or that the team agreed on a specific API versioning strategy. Without persistent memory, agents repeat mistakes, contradict earlier decisions, and waste tokens re-discovering context.
The Solution
sqlew stores your architectural decisions in a structured SQL database. When a new session starts, the AI agent queries past decisions in milliseconds — not by reading through scattered Markdown files, but through efficient SQL lookups with metadata, tags, and similarity detection.
┌─────────────────────────────────────────────────────────────┐
│ Before sqlew │ After sqlew │
│───────────────────────────────│─────────────────────────────│
│ Session 1: "Use PostgreSQL" │ Session 1: "Use PostgreSQL"│
│ Session 2: "Use MongoDB?" │ → decision recorded │
│ Session 3: "Use PostgreSQL" │ Session 2: query → got it │
│ (same debate, every time) │ Session 3: query → got it │
│ │ (instant recall) │
└─────────────────────────────────────────────────────────────┘
sqlew is built on the Model Context Protocol (MCP), so it works with any MCP-compatible AI coding tool.
This software does not send any data to external networks. We NEVER collect any data or usage statistics.
Quick Start
1. Install
npm install -g sqlew
2. Setup
Choose the setup that matches your environment:
Claude Code (Plugin)
claude plugin marketplace add sqlew-io/sqlew-plugin
claude plugin install sqlew
The plugin automatically configures MCP server, Skills (Plan Mode guidance), and Hooks (automatic decision capture).
Codex CLI
See sqlew-codex for Codex CLI integration.
Manual
Add to .mcp.json in your project root:
{
"mcpServers": {
"sqlew": {
"command": "sqlew"
}
}
}
The database (~/.config/sqlew/sqlew-shared.db) and config are auto-created on first run. See Shared Database for details.
3. Just use Plan Mode!
That's it. Every time you create a plan and get user approval, your architectural decisions are automatically recorded.
No special commands needed — just plan your work normally, and sqlew captures the decisions in the background.
Features
- Structured Records — Decisions stored as relational data with metadata, tags, layers, and version history
- Fast Queries — 2-50ms retrieval via SQL, even with thousands of decisions
- Duplicate Detection — Three-tier similarity scoring (0-100) prevents redundant decisions
- Constraint Tracking — Architectural rules and principles as first-class entities
- Auto-Capture — Hooks automatically record decisions from Plan Mode (Claude Code plugin)
- Multi-Database — SQLite (default), PostgreSQL, MySQL/MariaDB, or Cloud
- Git Worktree Ready — Each worktree shares the same context database
For Teams (sqlew.io)
Connect to sqlew.io for team-shared decisions:
Step 1: Get your API key
Visit sqlew.io and save your API key:
# ~/.config/sqlew/.sqlew.env (shared across all projects)
SQLEW_API_KEY=your-api-key
Step 2: Configure each project
# .sqlew/config.toml
[database]
type = "cloud"
[project]
name = "your-project-name"
Benefits:
- All team members share the same decision database
- Works seamlessly with Git worktree workflows
- No local database setup required
Performance
| Metric | Value |
|---|---|
| Query speed | 2-50ms |
| Concurrent agents | 5+ simultaneous |
| Storage efficiency | ~140 bytes/decision |
| Token savings | 60-75% vs Markdown ADRs |
Use Cases
- Architecture Evolution — Document major decisions with full context and alternatives considered
- Pattern Standardization — Establish coding patterns as constraints, enforce via AI code generation
- Cross-Session Continuity — AI maintains context across days/weeks without re-reading docs
- Multi-Agent Coordination — Multiple AI agents share architectural understanding
- Onboarding Acceleration — New AI sessions instantly understand project history
Documentation
| Guide | Description |
|---|---|
| ADR Concepts | Architecture Decision Records explained |
| Configuration | Config file setup, database options |
| Hooks Guide | Claude Code Hooks integration |
| Cross Database | Multi-database support |
| CLI Usage | Database migration, export/import |
Upgrade Guides
- Migrating to SaaS — Export local data to sqlew.io cloud
MCP Tools
7 action-based tools: decision, constraint, suggest, help, example, use_case, queue
All tools support action: "help" for documentation.
Support
Support development via GitHub Sponsors.
Version
Current version: 5.0.8
See CHANGELOG.md for release history.
What's New in v5.0.8:
- PR ADR enforcement — PreToolUse Hook blocks
gh pr createwithout ADR markers, file-grouped format - Codex CLI support — Works beyond Claude Code via sqlew-codex
- Plugin-first architecture — Simplified setup via sqlew-plugin
- Cloud backend — Connect to sqlew.io for team-shared decisions
License
Apache License 2.0 — Free for commercial and personal use. See LICENSE for details.
Links
Built with MCP SDK, better-sqlite3, and TypeScript.
FAQ
- What is the SQLew MCP server?
- SQLew is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for SQLew?
- This profile displays 66 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Data Analysis & Reporting
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ Use When
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid When
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.8★★★★★66 reviews- ★★★★★Chaitanya Patil· Dec 20, 2024
SQLew reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Layla Bansal· Dec 20, 2024
Useful MCP listing: SQLew is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★William Iyer· Dec 20, 2024
I recommend SQLew for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Yuki Verma· Dec 12, 2024
According to our notes, SQLew benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yusuf Dixit· Dec 8, 2024
SQLew has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Yuki Srinivasan· Dec 4, 2024
According to our notes, SQLew benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Kofi Abbas· Nov 27, 2024
SQLew is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Olivia Khan· Nov 23, 2024
We wired SQLew into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Layla Menon· Nov 15, 2024
We evaluated SQLew against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Piyush G· Nov 11, 2024
I recommend SQLew for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
showing 1-10 of 66