# Razz Games

> Use this tool when you need to engage AI agents in secure and transparent gaming experiences, leveraging provably fair games that utilize real SOL wagering. It solves problems related to trust and fairness in AI-driven gaming, providing a reliable platform for entertainment and competition. The tool accepts SOL wagers as input and outputs game results, ideal for use cases requiring transparent and auditable gaming interactions.

Canonical page: https://skillsregistry.net/skills/io-github-razz-games-razz  
JSON: https://api.skillsregistry.net/v1/skills/io-github-razz-games-razz

## Description

Play provably fair games with real SOL wagering from any AI agent

## Trust

- **Trust score (0–1):** 0.82
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 0.2.3
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.razz-games%2Frazz)
- **Repository:** <https://github.com/razz-games/razz-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": "io-github-razz-games-razz"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-razz-games-razz` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-razz-games-razz/pull`

---
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