by sin5ddd
SQLew boosts multi-agent coordination with efficient SQLite design, cutting context sharing tokens by 96% for decision a
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.
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.
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.
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.
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
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
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
SQLew reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: SQLew is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend SQLew for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, SQLew benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
SQLew has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, SQLew benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
SQLew is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired SQLew into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated SQLew against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend SQLew for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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Design decisions, remembered by SQL — an MCP server for AI agents
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.
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.
npm install -g sqlew
Choose the setup that matches your environment:
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).
See sqlew-codex for Codex CLI integration.
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.
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.
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:
| Metric | Value |
|---|---|
| Query speed | 2-50ms |
| Concurrent agents | 5+ simultaneous |
| Storage efficiency | ~140 bytes/decision |
| Token savings | 60-75% vs Markdown ADRs |
| 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 |
7 action-based tools: decision, constraint, suggest, help, example, use_case, queue
All tools support action: "help" for documentation.
Support development via GitHub Sponsors.
Current version: 5.0.8
See CHANGELOG.md for release history.
What's New in v5.0.8:
gh pr create without ADR markers, file-grouped formatApache License 2.0 — Free for commercial and personal use. See LICENSE for details.
Built with MCP SDK, better-sqlite3, and TypeScript.
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
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ 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.