# minizinc-mcp

> Use this tool when you need to solve complex constraint satisfaction and optimization problems. It takes MiniZinc models as input and provides optimized solutions as output, making it ideal for use cases such as resource allocation, scheduling, and planning. This tool is particularly useful in contexts where mathematical modeling and constraint programming are required to find efficient solutions.

Canonical page: https://skillsregistry.net/skills/r33drichards-minizinc-mcp  
JSON: https://api.skillsregistry.net/v1/skills/r33drichards-minizinc-mcp

## Description

Enables solving constraint satisfaction and optimization problems using MiniZinc models via a single tool.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-06-12

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/oapwzkogok)
- **Repository:** <https://github.com/r33drichards/minizinc-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": "r33drichards-minizinc-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/r33drichards-minizinc-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/r33drichards-minizinc-mcp/pull`

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