human-mcp

MCP server that provides humans as MCP tools

overview

human-mcp is an MCP server that allows AI assistants to leverage human capabilities: it receives requests from AI assistants, displays instructions to humans, and returns responses from humans to the AI assistant.

Key features:

• Accepts tool execution requests (via STDIN) from MCP clients

• Write the instructions required for execution to a SQLite database

• The Streamlit application monitors SQLite, displays instructions to the human, and prompts for responses.

• Write the results of human input via Streamlit to SQLite

• The MCP server reads the results from SQLite and returns them to the client (via STDOUT) as an MCP response.

Related MCP server: Interactive Feedback MCP

Tools provided

1. human_eye_tool : A human eye is used to describe a situation or locate something specific.

2. human_hand_tool : A human using his or her hand to perform a simple physical manipulation.

3. human_mouth_tool : A human uses his mouth to say the specified words.

4. human_weather_tool : A human checks and reports the weather in your location.

5. human_ear_tool : A human uses his ears to hear sounds and describe the situation.

6. human_nose_tool : A human uses their nose to identify smells.

7. human_taste_tool : A human uses his mouth to taste food and describe its taste.

set up

Prerequisites

• Python 3.12 or higher

• uv

• SQLite3

Installation Instructions

1. Clone the repository

   git clone https://github.com/yourusername/human-mcp.git
   cd human-mcp

2. Create and activate the virtual environment

   uv venv
   source .venv/bin/activate

3. Install dependencies

   uv pip install .

How to use

1. Install MCP server

task install-mcp

1. Connect to MCP server from Claude

   "human-mcp": {
     "command": "uv",
     "args": [
       "run",
       "--with",
       "mcp[cli]",
       "mcp",
       "run",
       "$PATH_TO_REPOSITORY/human_mcp/mcp_server.py"
     ]
   }

2. Launch Streamlit UI in a second terminal

   task run-streamlit

3. Access the Streamlit UI in your browser (usually http://localhost:8501 )

4. Once you submit your request through your MCP client (e.g. Claude Desktop), the task will appear in the Streamlit UI.

5. Once you enter your response in the Streamlit UI and click the "Send Response" button, the response will be sent back to the MCP client.

Project Structure

human-mcp/
├── human_mcp/              # メインのPythonパッケージ
│   ├── __init__.py         # パッケージマーカー
│   ├── db_utils.py         # SQLite関連ユーティリティ
│   ├── tools.py            # ツール定義
│   ├── mcp_server.py       # MCPサーバー本体
│   └── streamlit_app.py    # Streamlit UI アプリ
├── human_tasks.db          # SQLite データベースファイル (実行時に生成)
├── pyproject.toml          # プロジェクト設定、依存関係
└── README.md               # このファイル

license

MIT

Notes

This project is intended for use as a joke. In actual operation, it is necessary to take into account the burden on human operators and response delays.