Content Ops MCP

An MCP (Model Context Protocol) server that gives an LLM client, such as Claude Desktop, real tools for a social media content calendar. The client can plan posts, move them through a workflow and push updates to a team channel through a webhook.

It shows the pattern of connecting an LLM to external data and services through MCP instead of prompt-only use:

• Data source: a SQLite content calendar (content_ops/store.py)

• External service: outgoing JSON webhooks, compatible with Slack/Discord-style incoming webhooks (content_ops/webhook.py)

• MCP layer: tools and a resource built with the official Python MCP SDK (server.py)

Tools and resources

Related MCP server: postforme-mcp-pro

Setup

Requires Python 3.10+ and the MCP Python SDK 2.x (MCPServer).

python -m venv .venv
.venv/Scripts/python -m pip install "mcp[cli]>=2.2,<3" httpx pytest pytest-asyncio   # Windows
# source .venv/bin/activate && pip install "mcp[cli]>=2.2,<3" httpx pytest pytest-asyncio   # macOS/Linux

Optional environment variables:

• CONTENT_OPS_DB: SQLite file path (default: content_ops.db next to server.py)

• CONTENT_OPS_WEBHOOK_URL: incoming webhook URL for notify_webhook

Run

.venv/Scripts/python server.py        # stdio transport
.venv/Scripts/mcp dev server.py       # test the tools in MCP Inspector
.venv/Scripts/python -m pytest        # unit tests

Claude Desktop

Add to claude_desktop_config.json (adjust the paths):

{
  "mcpServers": {
    "content-ops": {
      "command": "C:/path/to/content-ops-mcp/.venv/Scripts/python.exe",
      "args": ["C:/path/to/content-ops-mcp/server.py"],
      "env": { "CONTENT_OPS_WEBHOOK_URL": "https://hooks.slack.com/services/..." }
    }
  }
}

Example prompts: "Plan three Instagram reels for next week's derby and add them to the calendar", "What is still unpublished for the next 14 days?", "Mark item 2 as scheduled and notify the team."

Docker

docker build -t content-ops-mcp .
docker run -i --rm -v content-ops-data:/data -e CONTENT_OPS_WEBHOOK_URL=... content-ops-mcp

Notes

Portfolio/demo project by Sergen Şahin, built with AI pair programming in Claude Code. It is a demo, not a production system.