# MCP Real-World Tasks Reference

> MCP Real-World Tasks Reference — vorortai-mcp-real-world-tasks-reference. Use this tool when you need to understand and implement the MCP protocol for real-world tasks, such as sending requests to interact with the physical world and receiving structured evidence in response. It solves problems related to learning and demonstrating the protocol layer, providing a minimal and dependency-free example to build upon. Ideal for use cases requiring a toy example of the 2026-07-28 protocol, with inputs of task requests and outputs of structured evidence.

Canonical page: https://skillsregistry.net/skills/vorortai-mcp-real-world-tasks-reference  
JSON: https://api.skillsregistry.net/v1/skills/vorortai-mcp-real-world-tasks-reference

## Description

A minimal, dependency-free MCP server demonstrating the 2026-07-28 protocol with a toy example of sending a person to check something in the physical world and returning structured evidence, ideal for learning the protocol layer.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** productivity
- **Updated:** 2026-08-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ubvoloz8cc)
- **Repository:** <https://github.com/vorortai/mcp-real-world-tasks-reference>

## 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": "vorortai-mcp-real-world-tasks-reference"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vorortai-mcp-real-world-tasks-reference` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vorortai-mcp-real-world-tasks-reference/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