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io.github.sGuNk13/kuriflow-mcp

Official

by sGuNk13 · Python

Make AI tasks repeatable. Run with new data automatically — zero AI tokens per run.

Kuriflow MCP Server enables AI tasks to be repeatable by running with fresh data automatically, reducing token consumption per run. It coordinates model-context tasks so outcomes are refreshed over time without manual re-execution.

🛠️ Key Features

  • Repeats AI tasks with new data automatically
  • Zero AI tokens per run
  • Integrates via email, Google Drive, or scheduling
  • Lightweight MCP (Model Context Protocol) server implementation

🚀 Use Cases

  • Automating weekly or periodic AI tasks with updated inputs
  • Reusing previous task definitions across data refresh cycles
  • Token-efficient task execution for AI agents

⚡ Developer Benefits

  • Standards-based MCP server for model-context tasks
  • Clear separation of task definitions and data feeds
  • Easy integration with common data channels (email/Drive/scheduler)

⚠️ Limitations

  • Based on readme excerpt; may require external services for data delivery
  • Specific authentication or connector setup not fully disclosed here
  • Behavior depends on downstream AI provider policies and data formats

Topics

ai-toolsanthropicautomationclaudeclaude-aiclaude-codeclaude-code-pluginclaude-desktopclaude-skillsmcpmcp-servermodel-context-protocolrepetitive-tasksworkflowworkflow-automation
io.github.sGuNk13/kuriflow-mcp - agentage MCP Catalog