Timeline MCP Server
# Timeline MCP Server
[](https://www.npmjs.com/package/timeline-mcp)
[](https://opensource.org/licenses/MIT)
**Model Context Protocol (MCP) server for managing scheduled social media posts and content automation.**
Timeline MCP enables AI assistants like Claude to manage your content calendar through natural language. Schedule posts across multiple platforms (X/Twitter, Reddit, LinkedIn, Instagram, TikTok, YouTube), organize campaigns into tracks, and automate your content workflow.
## Quick Start
Add to your MCP settings (e.g., `~/Library/Application Support/Claude/claude_desktop_config.json`):
```json
{
"mcpServers": {
"timeline": {
"command": "npx",
"args": ["-y", "timeline-mcp"],
"env": {
"POSTY_WORKSPACE": "/path/to/your/workspace"
}
}
}
}
```
**Environment Variables:**
- `POSTY_WORKSPACE` (required): Path to your Posty workspace directory containing the SQLite database
## What is MCP?
[Model Context Protocol](https://modelcontextprotocol.io/) is an open protocol that enables AI assistants to securely interact with local and remote tools. This server implements the MCP specification to provide timeline management capabilities.
## Development
For local development:
```bash
# Install dependencies
npm install
# Build the project
npm run build
# Run locally
npm start
```
## Implementation
Built with **FastMCP** framework for cleaner code and better TypeScript support. Uses SQLite database storage via Drizzle ORM for reliable local data persistence.
## Features
### Track Management
- 📋 **List Tracks** - View all content tracks
- ➕ **Add Track** - Create new tracks for organizing campaigns
- 🗑️ **Remove Track** - Delete tracks and associated events
### Scheduled Events
- 📅 **Add Scheduled Event** - Schedule posts with prompts, timing, and platform
- 📊 **List Events** - Filter by track, status, platform, or date range
- ✏️ **Update Event** - Modify scheduled events before publishing
- ❌ **Remove Event** - Delete scheduled events
### Supported Platforms
- X (Twitter)
- Reddit (with subreddit targeting)
- LinkedIn
- Instagram
- TikTok
- YouTube
## Usage Example
Ask your AI assistant:
> "Schedule a product launch campaign for next week. Create 5 teaser posts on X leading up to the launch, with one post per day at 10 AM."
The AI will use Timeline MCP to:
1. Create or reuse a track for the campaign
2. Generate engaging prompts for each post
3. Schedule them at the specified times
4. Store everything in your local database
## Media Storage
Generated images and content are saved to:
```
{workspace}/tracks/{track_name}/{event_name}/
```
Each event folder contains an `info.json` with metadata about the event.
## Examples
See [examples.md](https://github.com/derekalia/timeline-mcp/blob/main/examples.md) for detailed usage examples including:
- Product launch campaigns
- Content series scheduling
- Event coverage
- Multi-platform posts
## Requirements
- Node.js 18+
- A Posty workspace with SQLite database
- MCP-compatible AI assistant (Claude Desktop, etc.)TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose: timeline_add_scheduled_event and timeline_update_scheduled_event handle event creation/modification, timeline_remove_scheduled_event handles deletion, timeline_list_scheduled_events handles listing with filtering, timeline_add_track and timeline_remove_track manage tracks, and timeline_list_tracks lists tracks. There is no overlap or ambiguity between tools.
All tools follow a consistent 'timeline_verb_noun' pattern with snake_case, such as timeline_add_scheduled_event, timeline_list_tracks, and timeline_remove_track. This predictable naming convention makes it easy to understand each tool's function at a glance.
With 7 tools, this server is well-scoped for managing timeline tracks and scheduled events. It provides a complete set of operations (add, list, update, remove for both tracks and events) without being overly complex or too sparse, fitting typical use cases effectively.
The toolset offers full CRUD coverage for both tracks (add, list, remove) and scheduled events (add, list, update, remove), including advanced filtering for events. There are no obvious gaps; agents can manage the entire lifecycle of timeline data without dead ends.