gx-mcp-server
Exposes Great Expectations data quality validation tools to LLM agents via the Model Context Protocol (MCP), enabling programmatic data loading, validation rule definition, and quality check execution.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Supports HTTPS but doesn't enforce it by default. Recommends reverse proxy for production. Good secret handling via env vars. No fine-grained authorization scopes.
⚡ Reliability
Best When
You need LLM agents to perform data quality checks on structured data sources with flexible validation rules
Avoid When
You need a mature, production-ready data quality platform with enterprise support and guaranteed SLAs
Use Cases
- • Automated data quality validation in AI workflows
- • LLM-driven data profiling and expectation generation
- • Integration of data quality checks into agent pipelines
- • Validating CSV, Snowflake, and BigQuery data sources
- • Real-time data validation via HTTP API
Not For
- • Direct human interaction without an LLM intermediary
- • Large-scale production data pipelines without proper infrastructure
- • Systems requiring guaranteed uptime SLAs
- • Applications needing complex multi-step authentication flows
Interface
Authentication
Supports Basic Auth (username/password) and Bearer tokens (JWT validation). No authentication by default.
Pricing
Free open-source software (MIT license)
Agent Metadata
Known Gotchas
- ⚠ CSV size limit of 50MB by default (configurable up to 1GB)
- ⚠ Stores only last 100 datasets/results in memory
- ⚠ No persistent storage by default (SQLite option available)
- ⚠ Rate limiting must be configured separately
Alternatives
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Scores are editorial opinions as of 2026-03-17.