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Arun-kc/schemabrain

by Various

The trust and intelligence layer between AI agents and your database. Read-only by architecture, semantic knowledge graph + audit log, MCP-native.

A

MCP

Arun-kc/schemabrain

Added 18 June 2026

#agent-tools #agent-trust #agentic-ai #ai-agents #ai-security #audit-log #claude #database-security

Overview

Schemabrain acts as a read-only intermediary between AI agents and databases, using a semantic knowledge graph to provide context and an audit log for transparency. It is built as an MCP-native tool in Python, enabling agents to query structured data without write access.

Best for

Best for
Developers building AI agents that need safe, contextual database access

Use cases

  • Querying databases with natural language through AI agents
  • Enforcing read-only access for agent interactions
  • Auditing agent queries with a semantic context layer

How to use

Install

uvx schemabrain init

Tested with

Claude Desktop, Claude Code, Cursor, Windsurf, Cline, ChatGPT, VS Code

Notes

Schemabrain acts as a read-only intermediary between AI agents and databases, using a semantic knowledge graph to provide context and an audit log for transparency. It is built as an MCP-native tool in Python, enabling agents to query structured data without write access.

8 stars on GitHub. Last updated 2026-06-18. Licensed Apache-2.0.

Use cases

  • Querying databases with natural language through AI agents
  • Enforcing read-only access for agent interactions
  • Auditing agent queries with a semantic context layer

Pros

  • Read-only architecture prevents accidental data modification
  • Semantic knowledge graph improves query accuracy
  • MCP-native design integrates with existing agent frameworks

Cons

  • Limited to read-only operations, no write support
  • Requires setup of semantic knowledge graph
  • Relatively new project with small community (8 stars)

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Read-only architecture prevents accidental data modification
  • Semantic knowledge graph improves query accuracy
  • MCP-native design integrates with existing agent frameworks

Cons

  • Limited to read-only operations, no write support
  • Requires setup of semantic knowledge graph
  • Relatively new project with small community (8 stars)
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