home / mcp / agent construct mcp server
Exposes MCP-compliant tools and context management for AI applications.
Configuration
View docs{
"mcpServers": {
"ai-mcp-garage-agent_construct": {
"command": "python",
"args": [
"-m",
"mcp_server"
],
"env": {
"ENABLE_AUTH": "false",
"MCP_VERSION": "1.0",
"SERVER_HOST": "localhost",
"SERVER_PORT": "8000",
"TOOL_DISCOVERY_ENABLED": "true"
}
}
}
}Agent Construct is a Model Context Protocol (MCP) server that standardizes how AI applications access tools and context. It provides a central, scalable interface for tool discovery, execution, and context management so you can integrate AI workflows with a consistent MCP API.
You run the MCP server locally or remotely and connect your MCP client to it. The server exposes tools and context management through a standardized MCP interface, enabling AI models to discover available tools, request their execution, and receive real-time updates about context changes.
Prerequisites you need before installing: Python 3.8 or higher and the pip package manager.
# Clone the MCP server repository
git clone https://github.com/yourusername/agent_construct.git
cd agent_construct
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
# Create a .env file in the root directory with the following values
# Server Configuration
SERVER_HOST=localhost
SERVER_PORT=8000
# MCP Protocol Settings
MCP_VERSION=1.0
TOOL_DISCOVERY_ENABLED=true
# Security Settings
ENABLE_AUTH=false # Enable for productionConfiguration and runtime behavior are designed to be straightforward. The server uses a Python-based backend with a modular architecture so you can add new tools without changing core protocol handling.
Runtime guidance: after setting up the environment and credentials, start the MCP server to begin serving tools and context data.
Tool for web searching via Gemini, enabling live web data access for AI workflows.