Agent Construct

Exposes MCP-compliant tools and context management for AI applications.

python

13

GitHub Stars

python

Language

6 months ago

First Indexed

2 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": {
    "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.

How to use

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.

How to install

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 production

Additional sections

Configuration 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.

Available tools

web_search

Tool for web searching via Gemini, enabling live web data access for AI workflows.