AI Agent Generator MCP

MCP Server that enables AI to create other AI agents through natural language - similar to SmythOS Agent Weaver. Provides tools for agent specification parsing, workflow generation, component mapping, and agent deployment.

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "arturwyroslak-ai-agent-generator-mcp": {
      "command": "python",
      "args": [
        "-m",
        "src.server"
      ]
    }
  }
}

This MCP server lets you guide AI to design and deploy new AI agents through natural language. It provides AI-driven analysis, a large library of ready-to-use components, and one-click deployment to multiple environments, helping you rapidly build and operate sophisticated agent systems.

How to use

You interact with the server through an MCP client to create agents, attach smart components, generate chat interfaces, deploy across environments, and run comprehensive tests. Start by creating an agent with AI-powered analysis, then progressively assemble its capabilities with smart components that auto-configure based on context. You can generate a responsive chat interface for user interactions, deploy the agent with a single action, and monitor performance with built-in testing.

How to install

Prerequisites: Python and a working MCP client environment. You will install dependencies and run the MCP server locally.

# Clone the repository
git clone https://github.com/arturwyroslak/ai-agent-generator-mcp.git
cd ai-agent-generator-mcp

# Install dependencies
pip install -r requirements.txt

# Run the MCP server
python -m src.server

Additional notes

Configuration supports integration with Smithery.ai through a compatible smithery.yaml file. If you are connecting to Smithery.ai, ensure your environment contains the necessary configuration to enable seamless operation with the MCP workflow features.

Security and practice tips: manage access to the MCP client, monitor agent performance through the built-in testing tools, and keep dependencies up to date to leverage the latest AI capabilities and component sets.

Tools and capabilities

This MCP server exposes several practical tools to build and manage AI agents:

create_agent — Create agents with AI-powered analysis to extract requirements and plan capabilities.
add_component_to_agent — Smart component integration with auto-configuration to match the agent’s needs.
generate_chat_interface — Produce attractive, responsive chat interfaces for user interaction.
deploy_agent — Deploy agents to multiple environments with a single action.
test_agent — Run comprehensive tests and gather performance insights to guide optimization.

Available tools

create_agent

Create agents with AI-powered analysis to extract requirements and plan capabilities.

add_component_to_agent

Smart component integration with auto-configuration to match the agent’s needs.

generate_chat_interface

Produce attractive, responsive chat interfaces for user interaction.

deploy_agent

Deploy agents to multiple environments with a single action.

test_agent

Run comprehensive tests and gather performance insights to guide optimization.