io.github.iflytek/dolphin-mcp-pilot MCP Server
io.github.iflytek/dolphin-mcp-pilot
AI agent interface to Apache DolphinScheduler with 53+ tools for workflow orchestration, scheduling, and data operations.
What is the io.github.iflytek/dolphin-mcp-pilot MCP server?
The dolphin-mcp-pilot MCP server exposes 53+ tools for Apache DolphinScheduler, enabling AI agents to create workflows, manage schedules, control process instances, view logs, and perform advanced operations beyond basic read-only access. It supports multi-tenant authentication and provides both high-level workflow operations and raw API passthrough for edge cases.
dolphin-mcp-pilot is a production-ready MCP server that gives AI agents comprehensive control over Apache DolphinScheduler. It covers project and workflow management, DAG creation, schedule lifecycle (create/online/offline/delete), instance control (pause/resume/rerun/rerun-from-failure), resource management, log viewing, and monitoring. Use it to automate complex data pipeline operations, investigate failures, backfill missing data, and manage multi-tenant DolphinScheduler deployments.
How to install io.github.iflytek/dolphin-mcp-pilot
Copy-paste configuration for popular MCP clients.
DS_URLrequiredDolphinScheduler API base URL (e.g. http://localhost:12345/dolphinscheduler)
DS_TOKENDolphinScheduler API token (leave empty to use DS_USER/DS_PASSWORD instead)
DS_USERDolphinScheduler username (alternative to DS_TOKEN)
DS_PASSWORDDolphinScheduler password (used with DS_USER)
DS_TENANT_CODETenant code (default: 'default')
Tools & capabilities
Tools this server exposes to the agent.
ds_list_process_instances— List workflow process instances with filtering and guidance hints for troubleshootingds_list_task_instances— List task instances within a workflow runds_get_latest_failure_log— Retrieve logs from the latest failed taskds_create_workflow— Create SQL or DAG workflowsds_set_schedule— Configure cron-based schedules for workflowsds_online_schedule— Activate a workflow scheduleds_offline_schedule— Deactivate a workflow scheduleds_delete_schedule— Delete a workflow scheduleds_complement_data— Backfill data for a date range with serial or parallel executionds_pause_process_instance— Pause a running workflow instanceds_resume_process_instance— Resume a paused workflow instanceds_rerun_process_instance— Rerun a workflow instanceds_rerun_from_failure— Rerun a workflow from the point of failureds_delete_process_instance— Delete a workflow instanceds_force_task_success— Force a task to mark as successfulds_skip_failed_task— Skip a failed task in a workflowds_update_task_param— Update task parameters with flexible field namingds_manage_resources— View and update resource contentds_rollback_workflow_version— Rollback a workflow to a previous versionds_clone_workflow— Clone an existing workflow
Use cases
- Investigate failed workflow runs by retrieving task logs and failure details, then suggest remediation without making changes
- Backfill missing data for a date range by creating and running complement instances serially or in parallel
- Create and schedule new SQL or DAG workflows with cron expressions, then activate them for automated execution
- Manage multi-tenant DolphinScheduler access by running a single HTTP MCP service with per-request credential headers
- Pause, resume, rerun, or rerun-from-failure workflow instances to handle operational issues and recovery scenarios
io.github.iflytek/dolphin-mcp-pilot MCP server FAQ
It is a production-ready MCP server that exposes 53+ tools for Apache DolphinScheduler, enabling AI agents to create workflows, manage schedules, control instances, view logs, and perform advanced data pipeline operations.
Yes, it is open-source under the Apache 2.0 license.
Run the Docker Compose setup to start the HTTP MCP endpoint at http://localhost:8001/mcp/, then add it to your MCP client config with type 'sse' and your DolphinScheduler API token in the X-DS-Token header.
It supports API Token (X-DS-Token header) or username/password (X-DS-User and X-DS-Password headers), with multi-tenant per-request authentication.
Yes, you need a reachable Apache DolphinScheduler 3.x instance with an accessible API endpoint.
Projects and workflows, DAG creation, schedules, process instances, task instances, resources, logs, monitoring, and raw API passthrough.
README (reference)
Source of truth, from the repository.
dolphin-mcp-pilot
<div align="center"> </div>A production-ready MCP server for Apache DolphinScheduler (小海豚).
dolphin-mcp-pilot exposes 53+ tools for projects, workflows, DAG creation, schedules, instances, resources, logs, monitoring and raw API passthrough — designed for AI agents that need to operate DolphinScheduler beyond basic read-only usage.
🎯 Why this project?
Most public DolphinScheduler MCP servers only cover basic read/list/start/stop scenarios. This project is designed for real operations work:
- ✅ Create SQL / DAG workflows in one line
- ✅ Manage schedules (create / online / offline / delete)
- ✅ Control process instances (pause / resume / rerun / rerun-from-failure)
- ✅ View task logs, force task success / skip failed task
- ✅ Manage resources (view/update content)
- ✅ Roll back workflow versions, clone workflows
- ✅ Use raw API as a safety valve
- ✅ Support multi-tenant per-request auth
🚀 Key features
- 53+ tools covering most practical DS operations
- Two auth modes: API Token (
X-DS-Token) or User/Password (X-DS-User+X-DS-Password) - Multi-tenant HTTP mode: each caller can use its own credentials
- MCP 2.0 stateless HTTP with automatic compatibility for MCP 1.x clients
- Workflow creation: simple SQL and complex DAG workflows with multiple task types
- Schedule management (cron-based)
- Instance lifecycle control (pause/resume/rerun/rerun-from-failure/delete)
- Resource content management and version rollback / workflow clone
- Raw API passthrough for uncovered edge cases
🚀 Quick Start
Prerequisites
- A running DolphinScheduler 3.x instance whose API is reachable from Docker
- Docker with Compose v2 (
docker compose version) - A DolphinScheduler API token (recommended), or a username and password
# 1. Clone the repository
git clone https://github.com/iflytek/dolphin-mcp-pilot.git
cd dolphin-mcp-pilot
# 2. Configure environment
cp .env.example .env
# Edit .env — set DS_URL and DS_TOKEN (or DS_USER/DS_PASSWORD)
# Example DS_URL: http://your-dolphinscheduler-host:12345/dolphinscheduler
# 3. Build and start the service from this checkout
docker compose --profile dev up -d dolphin-mcp-pilot-dev
# 4. Confirm that the container is healthy
docker compose --profile dev ps
The MCP endpoint is now http://localhost:8001/mcp/ (the trailing slash is required).
Add it to an HTTP/SSE-capable MCP client:
{
"mcpServers": {
"dolphinscheduler": {
"type": "sse",
"url": "http://localhost:8001/mcp/",
"headers": { "X-DS-Token": "your_api_token" }
}
}
}
As a safe first check, ask your agent: “List my DolphinScheduler projects and workflows. Do not make any changes.” For client-specific configuration and username/password auth, see Client Config.
💡 Common use cases
| Scenario | Example request | Main tools |
|---|---|---|
| Investigate a failed run | “Find the latest failed workflow, show the failed task and its log, and suggest the next action without changing anything.” | ds_list_process_instances, ds_list_task_instances, ds_get_latest_failure_log |
| Backfill missing data | “Backfill 2026-08-01 through 2026-08-07 serially, starting from the validation task and including downstream tasks.” | ds_complement_data |
| Create and schedule a workflow | “Create a daily SQL workflow, add its cron schedule, and show me the definition before putting it online.” | ds_create_workflow, ds_set_schedule, ds_online_schedule |
| Give multiple agents controlled access | Run one HTTP MCP service while each caller supplies its own DolphinScheduler credentials. | Per-request X-DS-* headers |
The tools can also pause, resume, rerun, clone, and roll back workflows; manage resources; and
fall back to raw DolphinScheduler APIs for uncovered operations. Start with ds_help(category="quickstart")
inside your MCP client to discover the recommended workflow for each task.
📚 Documentation
| Document | Description |
|---|---|
| 📦 Installation | Docker Compose (dev/prod), from source, as package, run modes |
| ⚙️ Configuration | Environment variables, auth options, Compose tunables |
| 🚀 Deployment | Production deployment, Compose reference, verify, troubleshoot |
| 📊 Features | Feature comparison table, tool categories |
| 🔐 Client Config | MCP client setup (CodeBuddy, Claude Desktop, etc.), multi-tenant auth |
| 📖 API Reference | All 53+ tools, parameter conventions, error handling (中文) |
| ❓ FAQ | Common issues and solutions (中文) |
✨ What's new
- MCP 2.0: supports the stateless 2026-07-28 protocol while keeping legacy handshake clients and stdio configurations working.
- Guided troubleshooting:
ds_list_process_instancesattaches anext_actionhint to RUNNING/FAILURE instances, pointing agents tods_list_task_instancesto inspect individual task nodes. - Reliable backfill ordering: serial complement uses the
complementStartDate/complementEndDaterange format so DolphinScheduler generates instances in strict day-by-day order. - Flexible task params:
ds_update_task_paramaccepts bothsnake_caseandcamelCasefield names and reports ignored fields.
🤝 Contributing
Contributions are welcome. See CONTRIBUTING.md for project changes, or follow the example contribution guide to share a tested MCP client configuration.
Used dolphin-mcp-pilot for something real? Write it up in cases/ — a gallery of
community usage stories (agent-driven DolphinScheduler ops), each linked to a public post.
📄 License
🙏 Acknowledgments
Built with the official MCP Python SDK and inspired by the Apache DolphinScheduler community.
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