Great Expectations MCP Server

Overview

The Great Expectations MCP Server turns the powerful data‑quality framework Great Expectations into a first‑class tool that can be called by any LLM agent through the Model Context Protocol. By exposing core Great Expectations functionality—dataset loading, expectation definition, and validation execution—as MCP endpoints, the server removes a major friction point for AI‑driven data pipelines: agents no longer need custom code or SDKs to perform rigorous quality checks. Instead, they can issue simple text commands that the server translates into Great Expectations operations and return structured results for further processing or decision‑making.

At its core, the server provides a set of high‑level tools that mirror Great Expectations’ most common use cases. Developers can load CSV files, database tables (Snowflake or BigQuery), or inline data directly into the server’s in‑memory store. Once a dataset is loaded, an agent can create or modify an ExpectationSuite—a collection of data‑quality rules—on the fly. The server then runs validations, returning detailed pass/fail reports that include failed records and diagnostics. This workflow is invaluable for data‑centric LLM agents that need to verify inputs before downstream analysis, or for automated pipelines where quality gates are enforced programmatically.

Key capabilities include:

• Flexible data ingestion: CSV, URLs, or database URIs up to 1 GB (configurable), with optional SQLite persistence for long‑term storage.
• Dynamic expectation management: Create, update, or delete expectations without touching the filesystem, enabling agents to adapt rules on demand.
• Result retrieval: Synchronous or asynchronous validation results, with rich metadata for debugging and audit trails.
• Security & scalability: Basic or Bearer authentication, per‑minute rate limiting, CORS control, Prometheus metrics, and OpenTelemetry tracing.
• Multiple transport modes: STDIO for native LLM clients, HTTP for web or custom integrations, and an Inspector GUI for interactive debugging.

Real‑world scenarios that benefit from this server include automated data ingestion pipelines where an LLM orchestrates ETL steps, compliance checks in regulated industries, and conversational agents that validate user‑supplied datasets before performing analytics. By integrating seamlessly into existing MCP workflows, developers can add robust data‑quality checks to their AI agents with minimal friction, ensuring that downstream tasks operate on trustworthy data.