Granoflow MCP Server
MCP Server is best suited for native agents. It packages Granoflow’s capabilities as tools, allowing AI to call them under your authorization and within your local environment.
A Real‑World Scenario
Section titled “A Real‑World Scenario”If you’re using Codex, Claude Code, or a similar agent on your local machine, MCP is more reliable than copying and pasting task lists.
How to Decide What to Do Next
Section titled “How to Decide What to Do Next”| Your Situation | What to Check First | Next Step | | --- | --- | --- | | Don’t know where to start | Current page title and main entry points | Select only one item that relates to your current goal | | Operation results are wrong | Status, empty prompts, access logs, or sync progress | Go back one level and troubleshoot in order | | Worried about affecting data | Backup, sync, account, or permissions documentation | Stop first, confirm the scope, then proceed |
Boundaries
Section titled “Boundaries”Make sure the GranoFlow desktop app and local interface are available before configuring the MCP Server. MCP is not a cloud sync feature.
Differences from Connector
Section titled “Differences from Connector”| Feature | MCP Server | ChatGPT Connector | | --- | --- | --- | | Where it runs | Near your local AI tool | In ChatGPT / web environment | | Suitable tasks | Local agent automation, read/write tasks | Conversational queries and organization | | Key prerequisites | Desktop app, local interface, tool configuration | Connector authorization and account environment |
MCP’s strength is its closeness to the local workflow. That also means you need to understand local interfaces, access codes, and tool permissions.
Next Steps
Section titled “Next Steps”After reading this section, return to the task you’re working on and pick one minimal action: log an input, check a status, or open the relevant settings to confirm.