# connect4_mcp

> connect4_mcp — tolios-connect4-mcp. Use this tool when you need to solve Connect 4 problems using Monte Carlo methods, providing input parameters such as game state and receiving output moves with estimated probabilities, ideal for game development and AI strategy optimization contexts. It takes game boards as input and outputs recommended moves, enabling efficient decision-making in two-player games. This tool is suitable for applications requiring probabilistic game tree search and evaluation.

Canonical page: https://skillsregistry.net/skills/tolios-connect4-mcp  
JSON: https://api.skillsregistry.net/v1/skills/tolios-connect4-mcp

## Description

MCP for connect 4

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/tolios/connect4_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": "tolios-connect4-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tolios-connect4-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tolios-connect4-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