
tisu19021997/meta-prompt-mcp-server
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Turn any MCP Client into a "multi-agent" system (via prompting)
Обзор
This project is an implementation of the technique from the paper "Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding". At its core, this MCP transforms a standard Language Model (LM) into a dynamic, multi-agent system without the complex setup. It works by having the LM adopt two key roles: 1. : A high-level project manager that analyzes a complex problem, breaks it down into smaller, logical subtasks, and delegates them. 2. : Specialized agents (e.g., "Python Programmer," "Code Reviewer," "Creative Writer") that are "consulted" by the Conductor to execute each subtask. The magic is that this entire collaborative workflow is simulated within a single LM. The Conductor and Experts are different modes of operation guided by a sophisticated system prompt, allowing the model to reason, act, and self-critique its way to a more robust and accurate solution. It's like having an automated team of AI specialists at your disposal, all powered by one model.
README
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Meta Prompt MCP
This project is an implementation of the Meta-Prompting technique from the paper “Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding”.
At its core, this MCP transforms a standard Language Model (LM) into a dynamic, multi-agent system without the complex setup. It works by having the LM adopt two key roles:
- The Conductor: A high-level project manager that analyzes a complex problem, breaks it down into smaller, logical subtasks, and delegates them.
- The Expert: Specialized agents (e.g., “Python Programmer,” “Code Reviewer,” “Creative Writer”) that are “consulted” by the Conductor to execute each subtask.
The magic is that this entire collaborative workflow is simulated within a single LM. The Conductor and Experts are different modes of operation guided by a sophisticated system prompt, allowing the model to reason, act, and self-critique its way to a more robust and accurate solution. It’s like having an automated team of AI specialists at your disposal, all powered by one model.
Demo
Getting Started
1. Clone the Repository
First, clone this repository to your local machine.
git clone https://github.com/tisu19021997/meta-prompt-mcp-server.git .
cd meta-prompt-mcp-server
2. Install uv
This project uses uv, an extremely fast Python package manager from Astral. If you don’t have it installed, you can do so with one of the following commands.
Note: use which uv to know the path of your uv installation.
macOS / Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows (PowerShell):
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
For more details, see the official uv installation guide.
Usage
To use this Meta Prompt MCP server, you need to configure your client (e.g., Cursor, Claude Desktop) to connect to it. Make sure to replace the placeholder paths with the actual paths on your machine.
Cursor
Add the following configuration to your mcp.json settings:
"meta-prompting": {
"command": "path/to/your/uv",
"args": [
"--directory",
"path/to/your/meta-prompt-mcp",
"run",
"mcp-meta-prompt"
]
}
Claude Desktop
Add the following configuration to your claude_desktop_config.json settings:
"meta-prompting": {
"command": "path/to/your/uv",
"args": [
"--directory",
"path/to/your/meta-prompt-mcp",
"run",
"mcp-meta-prompt"
]
}
Activating the Meta-Prompt Workflow
Установка
This server does not publish a one-line install command.
Open the repository installation guide Новичок в MCP? Начните с руководства на mcpskill.org →