Editing Blog/2025-10-17/Custom GPTs (section)

== Early Attempts ==

=== Notion MCP ===

This is a pretty reasonable use-case for an LLM, and at first I thought it must surely already have been built. In fact, [https://developers.notion.com/docs/get-started-with-mcp Notion does have a remote MCP] that you can use OAuth to authenticate into. This is a pretty good start, and you can then create a Custom GPT that reads from Notion pages and provides information and so on.

Two things that went wrong with this are:

# On read, it scans far too many pages to get to the relevant one
# It doesn't seem to want to write anything

The former is annoying because it makes everything rather slow, but the latter renders things useless. The Notion MCP page says that it should be able to read and write but when I follow the Notion MCP instructions to install it on ChatGPT in either Sync or Deep Research / Agent mode the LLM insists that it cannot write data.

=== Google Keep MCP ===

She'd been using Google Keep, for which you can build an MCP using an unofficial Python API that also requires you to use the legacy API token flow. That whole thing seemed a bit iffy, so I decided it was probably a bad fit. She wasn't particularly wed to Google Keep. She just wanted the idea of separate notes that she could look at with some collating notion. She's used Apple's Notes before as well.

=== Notion API ===

Julie'd gotten quite far with the Custom GPT thing by the time I asked her what she was doing. The GPT Builder LLM had suggested that she get a Notion Integration token, then write the OpenAPI spec that matches Notion's API for the Custom GPT Actions feature and use that as a custom GPT. This wasn't such a bad idea, but when I observed her at work and tried to help out we realized it wasn't going to work. The OpenAPI spec that ChatGPT 5 generated wasn't close to compliant for the GPT Builder (which is quite strict) and the GPT Builder could not format the JSON payload well enough for the API to handle it.

But this is a pretty common kind of error for LLMs: they do better when told a simple thing like "to create a note use `createNote(body: string)`" than if you tell them a complicated thing with multiple nested JSONs and this and that. As it so happened, the generated OpenAPI spec simply wouldn't provide the Custom GPT with a tool that it could use to create notes. Or rather, it would create notes but they'd all be empty. It couldn't update them either.