# Project Data Standardization MCP

> Use this tool when you need to enforce consistent data formatting and organization across projects, solving issues of data inconsistency and incompatibility. It validates variable names, database structures, and file organization against project standards, providing smart naming suggestions as output. Ideal for use in data-intensive projects requiring standardized data management and collaboration.

Canonical page: https://skillsregistry.net/skills/999luan-mcp  
JSON: https://api.skillsregistry.net/v1/skills/999luan-mcp

## Description

This MCP ensures consistent naming conventions for variables, database structures, and file organization by validating against project standards and providing smart naming suggestions.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-19

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/m254irhunz)
- **Repository:** <https://github.com/999luan/mcp>

## 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": "999luan-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/999luan-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/999luan-mcp/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