linkedin-mcp-scheduler-ldraney (0.1.2)
Installation
pip install --index-url About this package
Standalone MCP server for LinkedIn post scheduling with a publisher daemon
linkedin-mcp-scheduler
Schedule and manage LinkedIn posts through conversation with an AI agent. Posts publish reliably on time — locally or in a container.
User Story
You're chatting with your AI agent. You say:
"Schedule that post for tomorrow at 9am"
Done. Then later:
"Show me my queue"
You see everything — what's pending, what published, what failed. You say:
"Actually push that one to Thursday and change the second paragraph"
The agent edits it in-place and reschedules. No cancelling, no re-creating, no UUIDs to copy-paste.
"That failed post from yesterday — retry it now"
Retried. Published. You never opened LinkedIn.
This is what linkedin-mcp-scheduler provides: a standalone MCP server with a reliable publishing daemon, full CRUD on your post queue, and a conversational UX designed for AI agents to manage your LinkedIn presence.
Why a Separate Project
linkedin-mcp wraps the LinkedIn API as MCP tools — posting, engagement, auth. It has basic scheduling, but scheduling is a different problem:
- It needs a daemon that runs continuously and publishes due posts. The API wrapper doesn't.
- It needs persistent state (a database) with its own lifecycle. The API wrapper is stateless.
- It needs to be containerizable — running in k8s with a volume-backed DB, HTTP transport, and env-based credentials. The API wrapper runs fine as a local stdio MCP.
- The UX surface area is large enough to be its own product: queue management, editing, rescheduling, retries, media scheduling, recurring posts.
Architecture
┌──────────────────────────────────────┐
│ Container (or local) │
│ │
│ MCP Server (HTTP/SSE) │
│ - schedule, list, edit, cancel │
│ - reschedule, retry, queue summary │
│ ↕ reads/writes │
│ SQLite DB ← persistent volume │
│ ↕ reads/writes │
│ Publisher Daemon (poll loop) │
│ - publishes due posts via SDK │
│ - retries failures │
│ │
└──────────────────────────────────────┘
Both processes share one SQLite database. In a container, the DB lives on a persistent volume. Locally, it defaults to ~/.linkedin-mcp-scheduler/scheduled.db.
Deployment Modes
- Local:
uv run linkedin-mcp-scheduler— stdio MCP for Claude Code, daemon as a background process or launchd job - Container: docker-compose with HTTP transport, volume-mounted DB, env-var credentials — ready to lift into k8s
MCP Tools
Core (from linkedin-mcp, to be extracted)
| Tool | Description |
|---|---|
schedule_post |
Schedule a text post (+ optional URL) for future publication |
list_scheduled_posts |
List scheduled posts, filterable by status |
get_scheduled_post |
Get details of a specific scheduled post |
cancel_scheduled_post |
Cancel a pending scheduled post |
Planned
| Tool | Description |
|---|---|
update_scheduled_post |
Edit content, URL, or visibility of a pending post in-place |
reschedule_post |
Change the scheduled time of a pending post |
retry_failed_post |
Retry a failed post, optionally at a new time |
queue_summary |
Formatted overview: counts by status, next due, recent failures |
schedule_post_with_image |
Schedule a post with an image attachment |
schedule_post_with_document |
Schedule a post with a document attachment |
schedule_post_with_poll |
Schedule a post with a poll |
Status
Early stage — establishing scope, setting up the project, and creating issues for the build-out.