GitHub user Hui-of-limin created a discussion: Dolphin MCP Pilot: Operate 
DolphinScheduler via AI Agents

# Dolphin MCP Pilot: Operate DolphinScheduler via AI Agents\n\nHi 
DolphinScheduler community ๐Ÿ‘‹\n\nWe've built **Dolphin MCP Pilot**, an MCP 
(Model Context Protocol) server that lets AI agents directly operate 
DolphinScheduler through natural language โ€” from creating workflows and 
managing schedules to troubleshooting failures and performing backfills.\n\n## 
What is it?\n\nAn integration layer that exposes **58 DolphinScheduler 
operations** as standardized MCP tools, enabling AI agents (Claude Desktop, 
Cursor, Cline, custom agents) to:\n\n- Create SQL/DAG workflows with a single 
sentence\n- Manage schedule lifecycle (create โ†’ online โ†’ offline โ†’ delete)\n- 
Control process instances (pause / resume / rerun / rerun-from-failure)\n- 
Intervene at task level (force success / skip failed nodes)\n- Query logs and 
troubleshoot failures with guided `next_action` prompts\n- Perform serial 
backfills with ordering guarantees 
(`complementStartDate`/`complementEndDate`)\n- Manage resource
 s and roll back workflow versions\n\n## Why this matters\n\nTraditional 
DolphinScheduler interaction requires:\n1. Opening the web UI\n2. Navigating 
multiple pages\n3. Manually clicking through operations\n4. Checking logs and 
states separately\n\nWith Dolphin MCP Pilot, you can tell your AI agent:\n\n> 
\"The ETL job failed yesterday. Why?\"\n\nThe agent automatically:\n1. Calls 
`ds_list_process_instances` โ†’ finds the failed instance\n2. Calls 
`ds_list_task_instances` โ†’ locates the failed task node\n3. Calls 
`ds_get_task_log` โ†’ pulls the error log\n4. Reports the root cause\n\nNo manual 
clicking, no page-hopping.\n\n## Architecture\n\n- **Protocol**: MCP over HTTP 
+ SSE\n- **Authentication**: Dual-mode (API Token or Username+Password)\n- 
**Compatibility**: DolphinScheduler 3.x (tested on 3.2.1)\n- **Deployment**: 
Docker / bare Python\n\n## Tool Coverage\n\n| Category | Tools | Examples 
|\n|---|---|---|\n| Project | 5 | `ds_create_project`, `ds_list_projects` |\n| 
Workflow | 14
  | `ds_create_workflow`, `ds_update_task_param`, `ds_clone_workflow` |\n| 
Schedule | 6 | `ds_set_schedule`, `ds_online_schedule`, `ds_complement_data` 
|\n| Instance | 13 | `ds_rerun_process_instance`, `ds_force_task_success` |\n| 
Resource | 10 | `ds_list_resources`, `ds_update_resource_content` |\n| 
Monitoring | 6 | `ds_get_task_log`, `ds_list_task_instances` |\n| Raw API | 4 | 
`ds_raw_get`, `ds_raw_post`, `ds_raw_put`, `ds_raw_delete` |\n\n## Quick 
Start\n\n```bash\ngit clone 
https://github.com/iflytek/dolphin-mcp-pilot.git\ncd dolphin-mcp-pilot\n\n# 
Configure\ncp .env.example .env\n# Edit .env: set DS_URL, DS_TOKEN (or 
DS_USER/DS_PASSWORD)\n\n# Run\ndocker compose --profile dev up 
-d\n```\n\nConnect your AI agent (example: Claude Desktop):\n\n```json\n{\n  
\"mcpServers\": {\n    \"dolphinscheduler\": {\n      \"url\": 
\"http://localhost:8001/mcp/\",\n      \"headers\": {\n        \"X-DS-Token\": 
\"your-token-here\"\n      }\n    }\n  }\n}\n```\n\nNow you can talk to your 
agent:\n\
 n> \"Create a daily SQL workflow in the data-team project that runs at 2 AM, 
querying `SELECT * FROM user_behavior WHERE dt = '${bizdate}'`, with 3 retries 
on failure.\"\n\nThe agent orchestrates `ds_create_workflow` โ†’ 
`ds_set_schedule` โ†’ `ds_online_schedule` automatically.\n\n## Real-World Use 
Cases\n\n**Scenario 1**: Automated troubleshooting\n- Agent detects a failed 
workflow, traces the specific task node, pulls logs, and suggests 
fixes\n\n**Scenario 2**: Bulk backfills\n- \"Rerun all daily reports from July 
1 to July 31\" โ†’ agent calls `ds_complement_data` with serial 
ordering\n\n**Scenario 3**: Version control\n- \"Roll back user_analysis 
workflow to the previous version\" โ†’ agent calls 
`ds_rollback_workflow_version`\n\n## Roadmap\n\n- DolphinScheduler 2.x 
compatibility\n- Pre-built DAG templates (CDC, feature engineering, report 
distribution)\n- Multi-agent orchestration (scheduling agent โ†” data quality 
agent โ†” alerting agent)\n\n## Links\n\n- **GitHub**: https://
 github.com/iflytek/dolphin-mcp-pilot\n- **License**: Apache-2.0\n- **Docs**: 
[Installation](https://github.com/iflytek/dolphin-mcp-pilot/blob/main/docs/INSTALLATION.md)
 ยท 
[Configuration](https://github.com/iflytek/dolphin-mcp-pilot/blob/main/docs/CONFIGURATION.md)
 ยท 
[Features](https://github.com/iflytek/dolphin-mcp-pilot/blob/main/docs/FEATURES.md)
 ยท [API 
Reference](https://github.com/iflytek/dolphin-mcp-pilot/blob/main/docs/API.md)\n\n---\n\nWe'd
 love to hear feedback from the community, especially on:\n- API compatibility 
issues with different DS versions\n- Authentication patterns in production 
environments\n- Feature requests for operations not yet covered\n\nThis is an 
open-source project under Apache-2.0. Contributions, issues, and discussions 
are welcome!\n

GitHub link: https://github.com/apache/dolphinscheduler/discussions/18547

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