kayla’s scrapbooklet’s talk ↗
03 / a project pulled from the pocket
MCP connectors

Give AI a door. And a set of keys.

Connecting an assistant to a real business system means deciding what it can see, what it can change and how every action can be understood afterwards.

focus

Integration architecture · backend · permissions

built with

Python · FastAPI · MCP · JSON-RPC · Audit logs

the whole journey, at a glance.

a little map of the productfollow the thread ↘

A request arrives through a defined tool interface.

Interactive product-flow illustration · tap a step to explore

01 / the starting point

What needed to work.

Two backends needed similar integration plumbing, but very different permissions. Treating them identically would hide the most important difference.

02 / my part

The work behind it.

I worked on a common Python/FastAPI MCP-server foundation and its adapters for the Lark CRM and NestJS web backend, including authentication, audit logging and access controls.

03 / an intentional choice

The decision that mattered.

Reuse the server foundation while keeping permission policies distinct. The CRM connector supports controlled administrative writes; the REST connector is read-only, with dry-run and kill-switch controls around riskier paths.

a note I’m taking with me

The boundary is part of the product. A smaller, explicit tool surface is easier to reason about than a connector that can do everything.

04 / project context

A little context.

Sanitized integration case study with a public implementation. Backend names describe architecture; no credentials, internal endpoints or customer data appear here.