MCP Server for Gemini CLI Agent Orchestration

This repository hosts a minimal Flask server designed for agent orchestration via Gemini CLI and other AI tools. It provides a clean, secure interface for exposing callable tools, validating agent inputs, and enabling reproducible workflows across contributors.

🔧 What It Does

• Exposes tools via HTTP endpoints for Gemini CLI and Manus AI agents
• Hosts a tool_registry.json for agent introspection and schema validation
• Supports modular, agent-free testing via main.py
• Deploys seamlessly to Render for public access

🧠 Why It Exists

This MCP (Modular Command Processor) server is part of a broader effort to make AI agent workflows:

• Contributor-friendly: Easy to onboard, test, and extend
• Modular: Tools are isolated, auditable, and reusable
• Secure: No secrets in Git history; .env is excluded and managed locally
• Agent-ready: Compatible with Gemini CLI, Claude, Manus, and other orchestration platforms

🚀 How to Use It

For Contributors:

• Clone the repo and run main.py locally to simulate agent calls
• Add new tools to tool_registry.json and expose them via Flask routes
• Use requirements.txt to manage dependencies

For Agents:

• Gemini CLI can call tools via HTTP once deployed to Render
• Agents can introspect available tools via tool_registry.json
• Supports prompt chaining, validation, and debug workflows

🌐 Deployment

This server is ready for deployment to Render. Once live, agents can access it via a public URL and begin orchestrating workflows.

📁 Key Files

• main.py: Flask server with exposed tools
• tool_registry.json: Tool definitions and schemas
• requirements.txt: Python dependencies
• render.yaml: Render deployment config
• .gitignore: Ensures .env and other sensitive files are excluded

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This is the foundation for scalable, agent-driven automation. Whether you're testing locally or deploying to production, this repo gives you the tools to build, validate, and orchestrate AI workflows with confidence.