Been working with MCP for a while and there's stuff that annoys me every week. Wondering if it's just me or if everyone's hitting the same things.
Two questions:
Sharing this because I think a lot of SaaS teams are about to walk into the same wall.
Before this I was CPO at an enterprise SaaS. We had auth, subscriptions, dashboards, the whole thing working fine for human users logging in. Then customers started asking if their AI agents could use our product on their behalf. Not "can you build us an AI feature." More like "can my Claude actually call your API the way I would."
I sat down to scope it.
Agents do not navigate UIs. They do not read your docs. They do not know which endpoint to call for what. They need a discoverable surface, machine-readable schemas, agent-scoped auth (your existing API token approach does not quite fit), and rate limits that make sense when one customer's agent fires a thousand calls in a minute. None of that fit our existing stack. It was basically a whole second stack sitting on top of the one we already had.
Honestly depressing to look at. Months of infra work for something that was not our core product. The kind of project that gets cut every time a "real" feature comes up. I knew what would happen if I stayed. We would ship a half-version, customers would build around it, we would never invest enough to do it right.
So I left and built it as a company.
What has surprised me since is that the work nobody wants to do inside their own SaaS is the work everyone actually needs done. The MCP protocol piece is small, the SDK does it fine. The hard part is the layer underneath. Per-customer auth. Per-customer scoping. Rate limits that do not punish the model for retrying. Observability so customers can see what their agent did. Tool descriptions tuned so the model picks the right one, which sounds trivial and is not.
CLI is open source and free locally if anyone wants to poke at it (bridge.ls). The thing I am most curious about right now, if you have shipped MCP at your SaaS or are mid-build: who actually drove the decision internally? Engineering pushing for it, product, CEO mandate? And on the other side of launch, how is adoption looking, and are you hitting the same multi-tenant problems I scoped out above once real customers start connecting their AI clients? Trying to figure out where the friction actually shows up post-ship and would rather hear it from people in it than guess.