# razz-mcp

> Use this tool when you need to enable AI agents to engage in provably fair games with real cryptocurrency wagering, solving the problem of secure and transparent gaming experiences. It accepts SOL (Solana) as input and provides a fair gaming environment as output. Ideal for use cases where AI agents require a trusted platform for competitive gaming with real stakes.

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

## Description

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

## Trust

- **Trust score (0–1):** 0.85
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/rtf3wqozgy)
- **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": "razz-games-razz-mcp"
    }
  }
}
```

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