Glama
Social Media MCP Server

{
  "twitter": {
    "thread": [
      "🔌 Unlock the full potential of AI with Model Context Protocol (MCP) servers! #AI #MachineLearning #MCP",
      "What is MCP? It's an open protocol that connects AI assistants like Claude to external tools and data sources - think of it as a USB-C port for AI applications. #AITools #TechInnovation",
      "Why build an MCP server? 🚀\n- Extend AI capabilities\n- Provide real-time data access\n- Create custom tools\n- Standardize AI integrations\n#AIInfrastructure #DeveloperTools",
      "Ready to build your own MCP server? Here's how to get started in 5 steps: #CodingTutorial #AIDevs",
      "Step 1: Set up your environment\n- Python 3.10+ or Node.js 18+\n- Install MCP SDK (`pip install \"mcp[cli]\"` or `npm install @modelcontextprotocol/sdk`)\n- Choose your transport (stdio/HTTP SSE)\n#PythonDev #JavaScript",
      "Step 2: Initialize your server\n```python\nfrom mcp.server.fastmcp import FastMCP\nmcp = FastMCP(\"my-service\")\n```\nor\n```javascript\nimport { Server } from '@modelcontextprotocol/sdk/server';\nconst server = new Server({ name: 'my-service' });\n```\n#CodeSnippet",
      "Step 3: Define your tools\n```python\n@mcp.tool()\nasync def get_data(param: str) -> dict:\n    # Your implementation here\n    return result\n```\nTools are functions that AI can call to perform actions! #APIDevelopment",
      "Step 4: Add resources (optional)\n```python\n@mcp.resource()\ndef get_schema() -> dict:\n    return database_schema\n```\nResources provide context to the AI without execution. #DataIntegration",
      "Step 5: Run your server\n```python\nif __name__ == \"__main__\":\n    mcp.serve(transport=\"stdio\")\n```\nConnect to Claude Desktop, Cursor IDE, or other MCP clients! #AIIntegration #DevTools",
      "Want to learn more? Check out the official docs at https://modelcontextprotocol.io and join the community! #OpenSource #AITutorial",
      "🔥 TODAY'S CHALLENGE: Build a simple weather MCP server that provides current conditions for any location. Share your creation with #BuildMCPChallenge! What MCP server will YOU build? 🛠️ #TechCommunity"
    ]
  },
  "mastodon": {
    "post": "🔌 Unlock the full potential of AI with Model Context Protocol (MCP) servers! #AI #MachineLearning #MCP #OpenSource\n\nWhat is MCP? It's an open protocol that connects AI assistants like Claude to external tools and data sources - think of it as a USB-C port for AI applications.\n\nWhy build an MCP server? 🚀\n- Extend AI capabilities with custom tools\n- Provide real-time data access to AI models\n- Create specialized integrations\n- Standardize how AI interacts with your systems\n\nReady to build your own MCP server? Here's how to get started in 5 steps:\n\n1️⃣ Set up your environment\n- Python 3.10+ or Node.js 18+\n- Install MCP SDK (`pip install \"mcp[cli]\"` or `npm install @modelcontextprotocol/sdk`)\n- Choose your transport (stdio/HTTP SSE)\n\n2️⃣ Initialize your server\n```python\nfrom mcp.server.fastmcp import FastMCP\nmcp = FastMCP(\"my-service\")\n```\nor\n```javascript\nimport { Server } from '@modelcontextprotocol/sdk/server';\nconst server = new Server({ name: 'my-service' });\n```\n\n3️⃣ Define your tools\n```python\n@mcp.tool()\nasync def get_data(param: str) -> dict:\n    # Your implementation here\n    return result\n```\nTools are functions that AI can call to perform actions!\n\n4️⃣ Add resources (optional)\n```python\n@mcp.resource()\ndef get_schema() -> dict:\n    return database_schema\n```\nResources provide context to the AI without execution.\n\n5️⃣ Run your server\n```python\nif __name__ == \"__main__\":\n    mcp.serve(transport=\"stdio\")\n```\nConnect to Claude Desktop, Cursor IDE, or other MCP clients!\n\nWant to learn more? Check out the official docs at https://modelcontextprotocol.io and join the community!\n\n🔥 TODAY'S CHALLENGE: Build a simple weather MCP server that provides current conditions for any location. Share your creation with #BuildMCPChallenge! What MCP server will YOU build? 🛠️ #ThursdayFiveList #AITools #TechInnovation #DeveloperTools"
  },
  "linkedin": {
    "post": "# Unlocking AI's Full Potential with Model Context Protocol (MCP) Servers\n\nIn today's rapidly evolving AI landscape, one of the biggest challenges is connecting AI models to the data and tools they need. That's where the Model Context Protocol (MCP) comes in - an open standard that's revolutionizing how we build AI-powered applications.\n\n## What is MCP?\n\nThe Model Context Protocol (MCP) is an open protocol that standardizes how AI assistants like Claude connect to external tools, data sources, and systems. Think of it as a USB-C port for AI applications - a universal connector that enables seamless integration between AI models and the digital world around them.\n\n## Why Build an MCP Server?\n\n🚀 **Extend AI capabilities** - Give AI models the ability to access real-time data, execute code, or control systems\n\n🔄 **Create custom tools** - Build specialized tools tailored to your specific business needs\n\n🌐 **Standardize integrations** - Use a consistent approach across different AI models and platforms\n\n💼 **Enhance productivity** - Automate complex workflows by connecting AI to your existing systems\n\n## Building Your First MCP Server: A Step-by-Step Guide\n\n### Step 1: Set Up Your Environment\n- Choose your language: Python 3.10+ or Node.js 18+\n- Install the MCP SDK:\n  - Python: `pip install \"mcp[cli]\"`\n  - TypeScript/JavaScript: `npm install @modelcontextprotocol/sdk`\n- Select your transport mechanism (stdio or HTTP with SSE)\n\n### Step 2: Initialize Your Server\n```python\n# Python example\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"my-service\")\n```\nor\n```javascript\n// TypeScript example\nimport { Server } from '@modelcontextprotocol/sdk/server';\n\nconst server = new Server({\n  name: 'my-service',\n  version: '1.0.0'\n});\n```\n\n### Step 3: Define Your Tools\nTools are functions that AI can call to perform actions:\n\n```python\nfrom pydantic import BaseModel\n\nclass DataRequest(BaseModel):\n    query: str\n    limit: int = 10\n\n@mcp.tool()\nasync def get_data(request: DataRequest) -> dict:\n    # Implementation to fetch data based on the request\n    result = await fetch_data(request.query, request.limit)\n    return result\n```\n\n### Step 4: Add Resources (Optional)\nResources provide read-only context to the AI:\n\n```python\n@mcp.resource()\ndef get_schema() -> dict:\n    return {\n        \"tables\": [\n            {\n                \"name\": \"users\",\n                \"columns\": [\"id\", \"name\", \"email\"]\n            }\n        ]\n    }\n```\n\n### Step 5: Run Your Server\n```python\nif __name__ == \"__main__\":\n    mcp.serve(\n        transport=\"stdio\",  # or \"http\" for web transport\n        host_id=\"claude-desktop\"\n    )\n```\n\n## Security Best Practices\n\nWhen building MCP servers, always:\n- Validate all inputs thoroughly\n- Implement proper authentication\n- Use rate limiting to prevent abuse\n- Follow the principle of least privilege\n- Log all operations for auditing\n\n## Real-World Applications\n\nMCP servers are already being used to:\n- Connect AI to database systems\n- Build browser automation tools\n- Create IoT control interfaces\n- Develop specialized research tools\n- Integrate with enterprise systems\n\n## Get Started Today\n\nThe MCP ecosystem is growing rapidly, with new tools and resources being added daily. Whether you're a developer looking to enhance your AI applications or a business seeking to integrate AI into your workflows, MCP provides a standardized, secure way to connect AI to your systems.\n\n🔥 **TODAY'S CHALLENGE**: Build a simple weather MCP server that provides current conditions for any location. Share your creation in the comments with #BuildMCPChallenge!\n\nWhat MCP server will YOU build? Let's innovate together!\n\n#AI #MachineLearning #MCP #AITools #TechInnovation #AIInfrastructure #DeveloperTools #OpenSource #AIIntegration #DataScience"
  }
}