# data-quality

> Use this tool when you need to enhance data accuracy and consistency in AI workflows, solving problems such as data inconsistencies, duplicates, and standardization issues. It provides access to 29 APIs for data quality, matching, enrichment, and standardization, taking in raw data as input and outputting refined and standardized data. This tool is ideal for use cases where data reliability is crucial, such as in conversational AI, data integration, and workflow automation.

Canonical page: https://skillsregistry.net/skills/interzoid-data-quality  
JSON: https://api.skillsregistry.net/v1/skills/interzoid-data-quality

## Description

An MCP server that exposes Interzoid's AI-powered data quality, matching, enrichment, and standardization APIs to AI agents and LLM applications.

This MCP server makes 29 Interzoid APIs discoverable and callable by any MCP-compatible client including Claude Desktop, Claude Code, Cursor, Windsurf, and other AI tools. AI agents can discover the available data quality tools and invoke them as needed during conversations and workflows.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-05-14

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/interzoid/data-quality)

## 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": "interzoid-data-quality"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/interzoid-data-quality` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/interzoid-data-quality/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