# zstar-mcp-server

> zstar-mcp-server — 8r4n-zstar-mcp-server. Use this tool when you need to create, manage, and interact with compressed archives in a standardized way, solving problems related to data compression, encryption, and verification. It provides a simple interface for AI assistants to perform archive operations, accepting commands and archive data as input and producing compressed archives, verification results, and error messages as output. Ideal for use cases involving data packaging, encryption, and distribution, particularly in environments compatible with MCP clients like OpenClaw and Claude Desktop.

Canonical page: https://skillsregistry.net/skills/8r4n-zstar-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/8r4n-zstar-mcp-server

## Description

An MCP (Model Context Protocol) server that exposes all features of the zstar archive utility. It allows AI assistants to create, extract, verify, encrypt, and sign compressed archives through a standardized tool interface.  The server uses stdio transport, compatible with OpenClaw, Claude Desktop, and any other MCP client.

## Trust

- **Trust score (0–1):** 0.94
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/8r4n/zstar-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": "8r4n-zstar-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/8r4n-zstar-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/8r4n-zstar-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