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MCP unreviewed MIT Self-run

Db Mcp

mcp-apelogic-ai-db-mcp · by apelogic-ai

db-mcp is a desktop MCP server that connects Claude to databases and continuously improves data analytics by learning from each query through an LLM-native semantic layer.

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Install

$ agentstack add mcp-apelogic-ai-db-mcp

Open-source listing, not yet scanned by AgentStack. Follow the source repository for install instructions.

Security review

⚠ Flagged

1 finding(s); flagged for manual review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures
  • high Pipes remote content directly into a shell (remote code execution).

What it can access

  • Network access Used
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Reliability & compatibility

Not yet reviewed
0 installs to date
no reviews yet
4mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

db-mcp

Your teams are copy-pasting company data into Claude. Give them a safe way to do it.

db-mcp is an open-source MCP server that connects Claude Desktop to your databases—with a self-improving semantic layer that gets smarter with every query.

> No infrastructure needed. Runs locally. Data never leaves your network.

Installation

curl -fsSL https://download.apelogic.ai/db-mcp/install.sh | sh

Why db-mcp?

  • Ask Claude, get answers — Stop waiting for reports. Query your data in natural language without SQL, copy-pasting schemas, or context-setting
  • Self-improving semantic layer — Auto-generates descriptions of your schema, then learns from every query to improve accuracy over time
  • Runs on your desktop — No cloud infrastructure, no data leaving your network. Uses your existing database credentials and access controls
  • Git-style collaboration — Data teams review and approve semantic layer improvements. You stay in control, users get accurate answers
  • Open source & auditable — MIT licensed, fully transparent, no vendor lock-in

Who is db-mcp for?

| Role | Benefit | |------|---------| | Business Teams | Ask Claude questions about your data and get accurate answers instantly—no SQL required | | Data Teams | Turn your warehouse into an agent-ready platform with a semantic layer that builds itself | | IT & Security | Govern AI data access with full interaction history, read-only enforcement, and cost guards |

How It Works

From install to insight in four steps:

  1. Install — Download db-mcp to your laptop. No infrastructure needed—runs locally in minutes
  2. Connect — Add data sources using your existing credentials. Your security posture stays intact
  3. Query — Ask Claude questions across your connected data. Every query is validated before execution
  4. Improve — The semantic layer learns from usage. Review and approve improvements with a git-style workflow

Quick Start

# Configure your database and Claude Desktop
db-mcp init

# Check configuration
db-mcp status

Supported Agents

Claude Desktop

The default setup works with Claude Desktop. Follow the Quick Start instructions above.

OpenClaw

db-mcp works with OpenClaw via mcporter:

# Install mcporter (MCP bridge for OpenClaw)
npm i -g mcporter

# Configure db-mcp for OpenClaw
db-mcp init

Or manually add to ~/.openclaw/workspace/config/mcporter.json:

{
  "mcpServers": {
    "db-mcp": {
      "command": "db-mcp",
      "args": ["start"]
    }
  }
}

Supported Databases

  • PostgreSQL
  • ClickHouse
  • Trino
  • MySQL
  • SQL Server

Security & Governance

  • Runs locally — Data stays on your machine, never routed through external servers
  • Read-only by default — Query validation and cost guards prevent accidental data modification
  • Uses existing RBAC — Connects with your database credentials and respects your access controls
  • Full audit trail — Complete interaction history for compliance and debugging
  • Open source — Fully auditable, MIT licensed

Commands

Setup & Configuration

| Command | Description | |---------|-------------| | db-mcp init [NAME] | Interactive setup wizard - configure database and Claude Desktop | | db-mcp status | Show current configuration status | | db-mcp config | Open config file in editor |

Connection Management

| Command | Description | |---------|-------------| | db-mcp list | List all configured connections | | db-mcp use NAME | Switch to a different connection | | db-mcp edit [NAME] | Edit connection credentials (.env file) | | db-mcp rename OLD NEW | Rename a connection | | db-mcp remove NAME | Remove a connection | | db-mcp all COMMAND | Run a command for all connections |

Server & Diagnostics

| Command | Description | |---------|-------------| | db-mcp start | Start the MCP server (stdio mode for Claude Desktop) | | db-mcp console | Start local trace console (view MCP server activity) | | db-mcp traces | Manage trace capture for diagnostics and learning |

Git Sync (Team Collaboration)

| Command | Description | |---------|-------------| | db-mcp git-init [NAME] | Enable git sync for an existing connection | | db-mcp sync [NAME] | Sync connection changes with git remote | | db-mcp pull [NAME] | Pull connection updates from git remote |

Migration

| Command | Description | |---------|-------------| | db-mcp migrate | Migrate from legacy storage format to new connection structure |

Configuration

Connection data is stored in ~/.db-mcp/:

~/.db-mcp/
├── config.yaml                      # Global config, active connection
└── connections/{name}/
    ├── .env                         # Database credentials (gitignored)
    ├── state.yaml                   # Onboarding state
    ├── schema/
    │   └── descriptions.yaml        # Table/column descriptions
    ├── domain/
    │   └── model.md                 # Domain model documentation
    ├── training/
    │   ├── examples.yaml            # Query examples (NL → SQL mappings)
    │   └── instructions.yaml        # Custom SQL generation rules
    └── .git/                        # Optional git sync for team sharing

Configuration Artifacts

| File | Purpose | |------|---------| | config.yaml | Global settings: active connection, preferences | | .env | Database URL and credentials (never committed to git) | | state.yaml | Onboarding progress (discovery → review → domain-building → complete) | | schema/descriptions.yaml | Human-readable descriptions of tables and columns | | domain/model.md | Business domain documentation for the LLM | | training/examples.yaml | Known-good query examples to guide SQL generation | | training/instructions.yaml | Custom rules and constraints for SQL generation |

Environment Variables

| Variable | Description | |----------|-------------| | DATABASE_URL | Connection string (required) | | CONNECTION_NAME | Override active connection | | CONNECTION_PATH | Override connection directory path | | LOG_LEVEL | Logging verbosity (default: INFO) |

Development

# Clone
git clone https://github.com/apelogic-ai/db-mcp.git
cd db-mcp

# Install dependencies
uv sync

# Run locally
cd packages/core
uv run db-mcp --help

# Lint
cd packages/core && uv run ruff check . --fix
cd packages/ui && bun run lint

# Build binary
cd packages/core
uv run python scripts/build.py

Testing

93 tests across three layers:

# Python unit tests (49 tests)
cd packages/core
uv run pytest tests/ -v

# UI unit tests (20 tests)
cd packages/ui
bun run test

# UI E2E tests (24 tests)
cd packages/ui
bunx playwright test

All tests run in CI via GitHub Actions on every push and PR. See [docs/testing.md](docs/testing.md) for the full testing guide.

Tool Coverage Check

Verify that 100% of MCP tools implemented in the codebase are exposed by the MCP server at runtime:

# Run the tool coverage audit (default SQL mode)
python scripts/tool_coverage_audit.py

# Test different configuration modes
python scripts/tool_coverage_audit.py --config api
python scripts/tool_coverage_audit.py --config shell
python scripts/tool_coverage_audit.py --config metabase

# Verbose output with all tool names
python scripts/tool_coverage_audit.py --verbose

# JSON output for CI integration
python scripts/tool_coverage_audit.py --json

The audit returns exit code 0 on success (100% coverage) and 1 on failure.

Project Structure

db-mcp/
├── packages/
│   ├── core/                # Main application (db-mcp)
│   ├── models/              # Shared Pydantic models (db-mcp-models)
│   └── ui/                  # Next.js control plane UI
├── docs/                    # Design documents
├── scripts/                 # Installation scripts
└── .github/workflows/       # CI/CD

Roadmap

See docs/ for design documents on planned features:

  • Metrics Layer - Semantic metric definitions (DAU, revenue, etc.)
  • Desktop App - Electron GUI with visual query builder
  • Knowledge Extraction - Learn from query traces automatically
  • Data Gateway - Unified access to multiple data sources

Resources

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.