https://github.com/aidecentralized/hackathon
https://github.com/aidecentralized/hackathon
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/aidecentralized/hackathon
- Owner: aidecentralized
- Created: 2025-06-05T22:17:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-06T14:38:15.000Z (about 1 year ago)
- Last Synced: 2025-06-06T15:35:20.659Z (about 1 year ago)
- Language: JavaScript
- Size: 40 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Launch a Context Agent for Your Company via MCP and the NANDA Registry
## **Company Agent Template**
Quickly build and deploy a context agent that makes your company discoverable and accessible to AI agents.
This template helps you create a context agent—a structured, AI-readable server that provides key information about your company. Once deployed, you can register it on the NANDA registry to make it accessible across the agentic web.
## **What You’ll Build**
A context agent that enables AI systems to query your company for:
- Company overview and mission
- Areas of focus, expertise, and services
- Contact information and external links
- Investment, hiring, or partnership details
- Any custom fields you define
## 📋 Prerequisites
- Node.js 18+ installed
- Basic familiarity with editing JSON/TypeScript files
- A GitHub account (for deployment)
## 🛠 Step-by-Step Setup Guide
### Step 1: Get the Template
1. **Fork or download this repository**
2. **Clone to your local machine:**
```bash
git clone
cd
```
3. **Install dependencies:**
```bash
npm install
```
### Step 2: Customize for Your Company
#### 📝 **File 1: `package.json` - Project Information**
Update these fields with your company info:
```json
{
"name": "your-company-mcp-server", // ← Change this
"description": "MCP server for Your Company information", // ← Change this
"author": "Your Company Name", // ← Change this
"keywords": [
"mcp",
"model-context-protocol",
"your-company-name", // ← Change this
"ai",
"typescript"
]
}
```
#### 🏢 **File 2: `src/index.ts` - Company Information**
This is the main file you need to customize. Find the `COMPANY_INFO` section (around line 10) and replace with your details:
```typescript
const COMPANY_INFO = {
name: "Your Company Name", // ← Change this
description: `Write a compelling description of your company here.
What do you do? What's your mission? What makes you unique?
This will be the main overview that AI systems will see.`, // ← Change this
focus_areas: [ // ← Change these
"Your Main Service/Product Area",
"Another Key Focus Area",
"Technology Stack You Use",
"Industry You Serve",
"Your Expertise Areas"
],
stage: "Description of your company stage", // ← Change this
// Examples: "Early-stage startup", "Established company", "Fortune 500", etc.
approach: "Your company's approach/methodology", // ← Change this
// Examples: "Customer-first design", "Agile development", "Data-driven decisions"
network: "Your network/partnerships description", // ← Change this
// Examples: "Global partner network", "Industry associations", "Client base"
website: "https://yourcompany.com", // ← Change this
contact: "hello@yourcompany.com" // ← Change this
};
```
#### 🤖 **File 3: `src/index.ts` - Server Name**
Find the server creation section (around line 31) and update:
```typescript
const server = new McpServer({
name: "your-company-server", // ← Change this
version: "1.0.0"
});
```
#### 📱 **File 4: `examples/mcp-client-config.json` - MCP Client Configuration**
Update the server name for MCP client integration:
```json
{
"mcpServers": {
"your-company": { // ← Change this
"comment": "Example configuration for MCP clients - update with your company details",
"command": "node",
"args": ["dist/index.js"],
"env": {
"PORT": "3000"
}
}
}
}
```
### Step 3: Test Your Server
1. **Build the project:**
```bash
npm run build
```
2. **Start the server:**
```bash
npm start
```
3. **Test it works:**
```bash
npm test
```
You should see your company information displayed for different categories!
### Step 4: Test with MCP Inspector
1. **Open MCP Inspector:**
```bash
npx @modelcontextprotocol/inspector
```
2. **In the inspector interface:**
- Transport Type: `Streamable HTTP`
- URL: `http://localhost:3000/mcp`
- Click **Connect**
3. **Test the `requestinfo` tool:**
- Try different categories: `"overview"`, `"focus"`, `"contact"`, `"all"`
- Verify your company information appears correctly
## 🚀 Deployment Options
### Option 1: Railway (Recommended - Free & Easy)
1. **Create Railway account:** [railway.app](https://railway.app)
2. **Connect your GitHub repo**
3. **Deploy automatically** - Railway detects the configuration
4. **Get your URL** - Use `https://your-app.railway.app/mcp` for AI integration
### Option 2: Docker
```bash
docker build -t your-company-mcp .
docker run -p 3000:3000 your-company-mcp
```
### Option 3: Other Platforms
The server works on any platform that supports Node.js. See `DEPLOYMENT.md` for detailed instructions.
## 🤖 Ready for AI Integration
Your deployed MCP server is now ready for AI systems to connect to at: `https://your-deployed-url.com/mcp`
For advanced users who want to integrate with AI systems, see the configuration examples in `examples/mcp-client-config.json`.
## 🎨 Advanced Customization
### Adding More Information Categories
In `src/index.ts`, you can add more cases to the switch statement:
```typescript
case "pricing":
responseText = `${COMPANY_INFO.name} Pricing Information:\n\n[Your pricing details]`;
break;
case "team":
responseText = `${COMPANY_INFO.name} Team:\n\n[Your team information]`;
break;
```
### Adding More Tools
You can add additional tools beyond `requestinfo`:
```typescript
server.tool(
"get_case_studies",
{
industry: z.string().optional().describe("Filter by industry")
},
async ({ industry }) => {
// Your implementation here
return {
content: [{
type: "text",
text: "Your case studies information"
}]
};
}
);
```
## 📁 Project Structure
```
├── src/
│ └── index.ts # Main server code (CUSTOMIZE THIS)
├── examples/
│ ├── mcp-client-config.json # MCP client config (CUSTOMIZE THIS)
│ └── test-client.ts # Test script
├── package.json # Project info (CUSTOMIZE THIS)
├── tsconfig.json # TypeScript config (NO CHANGES NEEDED)
├── Dockerfile # Docker config (NO CHANGES NEEDED)
├── Procfile # Railway config (NO CHANGES NEEDED)
└── README.md # This file
```
## 🆘 Troubleshooting
### Server won't start
- Make sure you ran `npm install`
- Check that Node.js 18+ is installed
- Try `npm run build` first
### Inspector can't connect
- Make sure server is running (`npm start`)
- Use the correct URL: `http://localhost:3000/mcp`
- Check the server logs for errors
### AI can't access deployed server
- Verify the deployment URL works: `https://your-url.com/health`
- Make sure you're using the `/mcp` endpoint
- Check CORS settings if accessing from browser
## 💡 Tips for Success
1. **Keep descriptions clear and concise** - AI systems work better with well-structured information
2. **Test thoroughly** - Use the inspector and test client to verify everything works
3. **Start simple** - Get the basic info working before adding advanced features
4. **Monitor logs** - Check server logs to debug issues
## 🎉 You're Done!
Congratulations! You now have a working, deployed MCP server for your company! Your server is:
✅ **Live and accessible** - Running on the web with a public URL
✅ **MCP compliant** - Ready for AI systems to discover and use
✅ **Production ready** - Includes health checks, error handling, and proper deployment
✅ **Customizable** - Easy to extend with more tools and information
This creates new possibilities for integrating your company information with AI systems and building innovative applications.
## 📚 Learn More
- [Model Context Protocol Documentation](https://modelcontextprotocol.io/)
- [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk)
- [NANDA: The Internet of AI Agents](https://nanda.media.mit.edu)
---
**Happy building! 🚀**
*Made with ❤️ for the developer community*