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Your agent asked a question. Which part was slow?

· 12 min read
DataZoo Team
flAPI Development Team

An agent's question travelling through flAPI as one continuous trace, down to the database

An agent asks your data API a question. Three seconds later it answers. Someone asks why three seconds, and the honest response is a shrug — because the only thing anybody can see is that a tools/call went in and JSON came out. Was it the template? The database? A JWKS refresh nobody remembers configuring? A second query you didn't know existed?

You can put a service mesh in front of flAPI and it will tell you the request took three seconds. It cannot tell you which part. Only flAPI knows that, and until now it wasn't saying.

It is now. flAPI emits OpenTelemetry traces for every request on every route, adopts the trace context an MCP agent sends alongside its tool call, and reports what DuckDB actually did underneath. All of it is off by default, and when you turn it on it exports no customer data unless you explicitly opt an endpoint in.

flAPI speaks MCP 2026-07-28 — and your slow queries finally stopped timing out

· 9 min read
DataZoo Team
flAPI Development Team

flAPI turns a long-running query across BigQuery, SAP, Iceberg, Postgres and S3 into a task that returns instantly

There's a moment every team hits when they wire an AI agent up to their real data warehouse. The demo works. The "show me last quarter's revenue by region" query works. And then someone asks for something that scans a few hundred million rows across BigQuery and SAP, the query takes ninety seconds, and the whole thing falls over — not because the query failed, but because something in the middle gave up waiting. A reverse proxy. A load balancer. The client's own timeout. The query was fine. The connection wasn't.

flAPI v26.08.31 is largely about that ninety-second gap — and about a bigger shift underneath it: MCP, the protocol AI clients use to call your tools, quietly stopped being a session protocol. Both of those changes landed in this release, and neither one breaks a single existing client.

If you're new here: flAPI turns SQL templates and a little YAML into REST APIs and MCP tools at the same time. You write a query; flAPI gives you an endpoint your services can call and a tool your AI agents can call, from the same file. It's a single C++ binary with DuckDB inside, so those tools reach BigQuery, Postgres, Iceberg, S3, SAP and 50-odd other sources.

Security roadmap shipped — MCP RBAC, prepared statements, PBKDF2, audit log

· 4 min read
DataZoo Team
flAPI Development Team

flAPI v26.05.17 is out. This release answers a recurring question from operators evaluating flAPI for AI-agent access to real data warehouses: "Is this safe to expose to MCP-driven agents against production?"

The answer is now: yes, with single-line opt-ins — flapii project init demos still work without auth, and every production control is one YAML key away.

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