# Great Expectations Data Quality Server

> Use this tool when you need to integrate robust data validation into automated workflows, exposing data-quality checks as callable tools for LLM agents to ensure data accuracy and consistency. It solves problems of data inconsistencies and inaccuracies by allowing programmatic validation of datasets against predefined rules. It accepts datasets and validation rules as inputs and outputs data quality check results, making it ideal for use cases requiring reliable data quality assurance.

Canonical page: https://skillsregistry.net/skills/davidf9999-gx-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/davidf9999-gx-mcp-server

## Description

Expose Great Expectations data-quality checks as callable tools for LLM agents. Load datasets, define validation rules, and run data quality checks programmatically to integrate robust data validation into automated workflows. Support multiple data sources, authentication methods, and transport modes for flexible deployment.

## Trust

- **Trust score (0–1):** 0.72
- **Verification tier:** verified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** devops-ci
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/davidf9999/gx-mcp-server)
- **Repository:** <https://github.com/davidf9999/gx-mcp-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "davidf9999-gx-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/davidf9999-gx-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/davidf9999-gx-mcp-server/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt