Timelinesai Automation

What This Skill Does

This skill automates Timelines AI operations using Composio's Timelines AI toolkit via Rube MCP. It's designed for users who want to programmatically manage Whats App communications, track customer interactions, and analyze team performance using Timelines AI. The skill requires an active Timelines AI connection and familiarity with Rube MCP.

When to Use

Automate Whats App message sending Track customer interactions Analyze team performance in Timelines AIIntegrate Timelines AI with other systems Fetch message history Manage contacts and groups

Key Features

Uses Rube MCP for Timelines AI automation Requires active Timelines AI connection Discovers available tools via RUBE_SEARCH_TOOLSChecks connection status via RUBE_MANAGE_CONNECTIONSExecutes tools using RUBE_MULTI_EXECUTE_TOOLProvides toolkit documentation for reference Automate Timelinesai operations through Composio's Timelinesai toolkit via Rube MCP. Toolkit docs: composio.dev/toolkits/timelinesai

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Timelinesai connection via RUBE_MANAGE_CONNECTIONS with toolkit timelinesai Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds Call RUBE_MANAGE_CONNECTIONS with toolkit timelinesai If connection is not ACTIVE, follow the returned auth link to complete setup Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows: RUBE_SEARCH_TOOLS queries: [{use_case: "Timelinesai operations", known_fields: ""}] session: {generate_id: true} This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS queries: [{use_case: "your specific Timelinesai task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["timelinesai"] session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL tools: [{ tool_slug: "TOOL_SLUG_FROM_SEARCH", arguments: {/ schema-compliant args from search results /} }] memory: {} session_id: "your_session_id"

Known Pitfalls

Always search first: Tool schemas change. Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools Schema compliance: Use exact field names and types from the search results Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({}) Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference