MCP Task Orchestrator is a server that enables AI assistants to manage and coordinate tasks. It benefits operations teams by automating workflows and integrating with AI agents. The tool connects to Python-based systems and supports Claude AI agents.
git clone https://github.com/EchoingVesper/mcp-task-orchestrator.gitThe mcp-task-orchestrator is a Model Context Protocol server designed to provide robust task orchestration capabilities for AI assistants. By leveraging this Claude Code skill, users can efficiently manage and coordinate tasks within AI workflows, enabling seamless interaction and operation of AI agents. This skill is particularly beneficial for developers and product managers looking to enhance their AI automation processes and improve overall workflow efficiency. One of the key benefits of the mcp-task-orchestrator is its ability to streamline complex task management, allowing teams to focus on higher-level strategic initiatives rather than getting bogged down in manual task coordination. While specific time savings are not quantified, the skill's intermediate implementation time of just 30 minutes suggests that users can quickly integrate it into their existing systems, leading to immediate productivity gains. This skill is ideal for developers and AI practitioners who are involved in building and managing AI agents. It fits well within AI-first workflows, where automation and orchestration are crucial for maximizing the potential of AI technologies. Practical use cases include automating data processing tasks, coordinating responses from multiple AI agents, and enhancing user interactions through more organized task management. With an intermediate complexity level, the mcp-task-orchestrator requires some familiarity with AI automation concepts but is accessible enough for those with a basic understanding of AI workflows. By incorporating this skill into your AI toolkit, you can unlock new efficiencies and elevate the capabilities of your AI assistants, making it a valuable addition to any automation strategy.
["1. **Define Your Workflow:** Identify the specific trigger (e.g., new email, task status change) and desired actions (e.g., task creation, assignment, email sending) in Sortd. Use Sortd's board and column names as references.","2. **Set Up MCP Task Orchestrator:** Connect the orchestrator to your Sortd workspace via API or webhook. Ensure you have admin access to configure automation rules.","3. **Customize Rules:** Use the prompt template to define rules like 'When [TRIGGER] occurs in Sortd, perform [ACTIONS].' Replace placeholders with your specific board names, team members, and templates.","4. **Test the Automation:** Run a test scenario (e.g., send a test email to your Sortd board) to verify the workflow executes as expected. Check Sortd for the created task and confirm all actions (assignments, reminders, emails) are triggered.","5. **Monitor and Optimize:** Review the 'Automated Workflow Execution Report' in the orchestrator dashboard. Adjust rules based on performance (e.g., tweak reminder timings or task assignments) to improve efficiency."]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/EchoingVesper/mcp-task-orchestratorCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use the MCP Task Orchestrator to automate the following workflow in Sortd: [DESCRIBE_THE_WORKFLOW]. For example: 'When a new lead email arrives in the [SALES_BOARD] board with a subject containing "Proposal Request", create a task in the [FOLLOW_UP_STAGE] column, assign it to [SALES_REP], and set a reminder for [DUE_DATE]. Include a follow-up email template for the sales rep to send.'
### Automated Workflow Execution Report: Lead Follow-Up Pipeline **Workflow Trigger:** New email received in Sortd's 'Inbox' board with subject line containing 'Proposal Request' from [email protected] **Actions Executed:** 1. **Task Creation:** A new task titled 'Follow up with Potential Client - Proposal Request' was automatically created in the 'Follow-Up' column of the 'Sales Pipeline' board. 2. **Assignment:** The task was assigned to Sarah Johnson (Sales Rep) with a priority level of 'High'. 3. **Context Attachment:** The original email thread and attachment (Proposal_Request.pdf) were linked to the task for reference. 4. **Reminder Setup:** A calendar reminder was scheduled for November 15, 2023, at 2:00 PM with a pre-written follow-up email template: ``` Subject: Quick Question About Your Proposal Request Hi [LEAD_NAME], Thank you for reaching out regarding our proposal. I wanted to check if you had any questions or needed additional details before we proceed. I'm available at [SALES_PHONE] or can schedule a call at your convenience. Best regards, Sarah Johnson Sales Representative [COMPANY_NAME] ``` 5. **Status Update:** The task status was set to 'Pending' with a note: 'Automated workflow executed via MCP Task Orchestrator. Next action: Send follow-up email by Nov 15.' **Outcome:** The sales team now has a structured, time-bound task with all necessary context attached, reducing the risk of missed follow-ups and improving response times. **Metrics:** - Average time from lead receipt to task creation: 2 minutes (vs. 30+ minutes manually) - Follow-up email sent within 24 hours of task assignment: 92% compliance rate - Reduction in duplicate or lost leads: 40% improvement **Recommendation:** Extend this workflow to include AI-powered urgency detection (e.g., emails with 'Urgent' in the subject line trigger immediate task assignment with a 2-hour reminder).
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