agent-install-templates

AI Agent Install Templates

Production-ready templates for packaging AI agents and automations as installable software. Every template is complete, annotated, and ready to copy into a project.

---

1. MCP Server (Node.js / TypeScript)

package.json

{
  "name": "@my-org/my-agent-mcp",
  "version": "0.1.0",
  "description": "MCP server that exposes my-agent capabilities as tools",
  "license": "MIT",
  "author": "my-org",
  "type": "module",
  "bin": {
    "my-agent-mcp": "./dist/index.js"
  },
  "main": "./dist/index.js",
  "files": [
    "dist"
  ],
  "scripts": {
    "build": "tsc",
    "dev": "tsc --watch",
    "start": "node dist/index.js",
    "lint": "eslint src/",
    "prepublishOnly": "npm run build"
  },
  "dependencies": {
    "@modelcontextprotocol/sdk": "^1.12.0",
    "zod": "^3.23.0"
  },
  "devDependencies": {
    "@types/node": "^22.0.0",
    "typescript": "^5.7.0",
    "eslint": "^9.0.0"
  },
  "engines": {
    "node": ">=18.0.0"
  },
  "keywords": [
    "mcp",
    "model-context-protocol",
    "ai-agent"
  ]
}

tsconfig.json

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "Node16",
    "moduleResolution": "Node16",
    "outDir": "./dist",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "declaration": true,
    "sourceMap": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist"]
}

src/index.ts

#!/usr/bin/env node

// MCP Server skeleton — registers tools that an LLM client can invoke.
// The MCP SDK handles stdio transport, JSON-RPC framing, and capability negotiation.

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

// --- Server instance ---------------------------------------------------
const server = new McpServer({
  name: "my-agent-mcp",       // Appears in client UI (Claude Desktop, etc.)
  version: "0.1.0",           // Keep in sync with package.json
});

// --- Tool: greet -------------------------------------------------------
// Replace this with your agent's real capability.
// Each tool gets a name, description (shown to the LLM), input schema, and handler.
server.tool(
  "greet",                                        // Tool name (lowercase, hyphen-separated)
  "Generate a greeting for the given name",       // LLM-facing description
  {
    name: z.string().describe("Name to greet"),   // Zod schema = JSON Schema for the LLM
  },
  async ({ name }) => {
    // Your agent logic goes here.
    // Return content as an array of text/image/resource blocks.
    return {
      content: [
        { type: "text", text: `Hello, ${name}! Welcome to my-agent.` },
      ],
    };
  }
);

// --- Tool: analyze (example with structured output) --------------------
server.tool(
  "analyze",
  "Analyze the provided text and return key insights",
  {
    text: z.string().describe("Text to analyze"),
    depth: z.enum(["quick", "thorough"]).default("quick").describe("Analysis depth"),
  },
  async ({ text, depth }) => {
    // Replace with your real analysis logic.
    const wordCount = text.split(/\s+/).length;
    const result = {
      wordCount,
      depth,
      summary: `Analyzed ${wordCount} words at ${depth} depth.`,
    };
    return {
      content: [
        { type: "text", text: JSON.stringify(result, null, 2) },
      ],
    };
  }
);

// --- Start server ------------------------------------------------------
async function main() {
  const transport = new StdioServerTransport();
  await server.connect(transport);
  // Server is now listening on stdin/stdout. The MCP client drives the conversation.
}

main().catch((error) => {
  console.error("Fatal:", error);
  process.exit(1);
});