flAPI: Instant SQL based APIs

flAPI is a powerful service that automatically generates read-only APIs for datasets by utilizing SQL templates. Built on top of DuckDB and leveraging its SQL engine and extension ecosystem, flAPI offers a seamless way to connect to various data sources and expose them as RESTful APIs.

⚡ Features

• Automatic API Generation: Create APIs for your datasets without coding
• MCP (Model Context Protocol) Support: Declarative AI tools alongside REST endpoints, speaking the latest MCP 2026-07-28 revision (dual-era: modern and legacy clients) — with the Tasks extension for long-running queries, typed schemas + structured results, OAuth discovery, per-tool RBAC, shadow/dry-run, response shaping, rate limiting, and a prompt-injection hygiene scanner
• Multiple Data Sources: Connect to BigQuery, SAP ERP & BW (via ERPL), Parquet, Iceberg, Postgres, MySQL, and more
• SQL Templates: Mustache-like syntax. Typed {{ params.X }} references on int/double/boolean/date/time/uuid/enum/email/string fields are bound as DuckDB prepared statements — SQL injection is structurally impossible for those sites
• Caching: DuckLake-backed cache with full refresh and incremental sync
• Production security: PBKDF2-SHA256 password hashing, config-driven CORS allowlist, per-user rate limiting, JSONL request audit log, TLS termination, startup config auditor — all opt-in via single-line YAML so flapii project init demos stay simple
• Easy deployment: Deploy flAPI with a single binary file
• Self-packaging: Fold an entire flapi config tree (YAMLs + SQL templates + small data files) into the binary itself via flapi pack. scp flapi-prod user@host becomes the whole deploy. Reproducible (SOURCE_DATE_EPOCH), notarisable on macOS via a reserved Mach-O segment, with a secret deny list (*.env, secrets/*, *.pem, *.key) enforced at pack time.
• Privacy-respecting telemetry: Anonymous startup/shutdown analytics with easy opt-out via --no-telemetry flag, FLAPI_NO_TELEMETRY env var, or flapi.yaml

📦 Install

The fastest way to try flAPI — no download, no Docker:

# Run the flapi server (note: "flapi" is taken on PyPI, so the package is "flapi-io")
uvx --from flapi-io flapi -c flapi.yaml

# Run the flapii CLI client (also bundled in flapi-io)
uvx --from flapi-io flapii

Or install permanently — one package gives you both commands:

pip install flapi-io   # installs both "flapi" and "flapii" commands

Pre-built binaries and Docker images are also available — see below.

🛠 Quick Start

The easiest way to get started with flAPI is to use the pre-built docker image.

1. Pull the docker image from the Github Container Registry:

> docker pull ghcr.io/datazoode/flapi:latest

The image is pretty small and mainly contains the flAPI binary which is statically linked against DuckDB v1.5.5. Details about the docker image can be found in the Dockerfile.

2. Run flAPI:

Once you have downloaded the binary, you can run flAPI by executing the following command:

> docker run -it --rm -p 8080:8080 -p 8081:8081 -v $(pwd)/examples/:/config ghcr.io/datazoode/flapi -c /config/flapi.yaml

The different arguments in this docker command are:

• -it --rm: Run the container in interactive mode and remove it after the process has finished
• -p 8080:8080: Exposes port 8080 of the container to the host, this makes the REST API available at http://localhost:8080
• -p 8081:8081: Exposes port 8081 for the MCP server (when enabled)
• -v $(pwd)/examples/:/config: This mounts the local examples directory to the /config directory in the container, this is where the flAPI configuration file is expected to be found.
• ghcr.io/datazoode/flapi: The docker image to use
• -c /config/flapi.yaml: This is an argument to the flAPI application which tells it to use the flapi.yaml file in the /config directory as the configuration file.

2.1 Enable MCP Support:

To enable MCP support, you can either:

Option A: Use the command line flag

> docker run -it --rm -p 8080:8080 -p 8081:8081 -v $(pwd)/examples/:/config ghcr.io/datazoode/flapi -c /config/flapi.yaml --enable-mcp

Option B: Configure in flapi.yaml

mcp:
  enabled: true
  port: 8081
  # ... other MCP configuration

3.1 Test the API server:

If everything is set up correctly, you should be able to access the API at the URL specified in the configuration file.

> curl 'http://localhost:8080/'


         ___
     ___( o)>   Welcome to
     \ <_. )    flAPI
      `---'    

    Fast and Flexible API Framework
    powered by DuckDB

3.2 Get an overview of the available endpoints:

The flAPI server creates embedded Swagger UI at which provides an overview of the available endpoints and allows you to test them. It can be found at

> http://localhost:8080/doc

You should see the familiar Swagger UI page:

The raw yaml Swagger 2.0 is also available at http://localhost:8080/doc.yaml

3.3 Test the MCP server:

If MCP is enabled, you can test the MCP server as well:

# Check MCP server health
> curl 'http://localhost:8081/mcp/health'

{"status":"healthy","server":"flapi-mcp-server","version":"0.3.0","protocol_version":"2024-11-05","tools_count":0}

# Initialize MCP connection
> curl -X POST http://localhost:8081/mcp/jsonrpc \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "initialize"}'

# List available tools
> curl -X POST http://localhost:8081/mcp/jsonrpc \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 2, "method": "tools/list"}'

🤖 MCP (Model Context Protocol) Support

flAPI now supports the Model Context Protocol (MCP) in a unified configuration approach. Every flAPI instance automatically runs both a REST API server and an MCP server concurrently, allowing you to create AI tools alongside your REST endpoints using the same configuration files and SQL templates.

Key Features

• MCP 2026-07-28 (dual-era): serves the latest stateless MCP revision (server/discover, per-request metadata, cacheable results, OAuth discovery via RFC 9728) alongside the legacy initialize/session protocol — existing clients keep working unchanged
• Long-running tools (Tasks extension): mark a tool async and slow queries return a task handle immediately instead of blocking the connection; the durable task store survives a restart, with tasks/get / tasks/cancel and per-caller isolation
• Typed, structured tool contracts: tool parameters advertise real types and constraints (int ranges, dates, uuid, enum, …), results carry machine-readable structuredContent, an outputSchema is learned after first use, and failures return actionable isError results the model can self-correct from
• Unified Configuration: Single YAML files can define REST endpoints, MCP tools, and MCP resources
• Automatic Detection: Configuration type is determined by presence of url-path (REST), mcp-tool (MCP tool), or mcp-resource (MCP resource)
• Shared Components: MCP tools and resources use the same SQL templates, parameter validation, authentication, and caching as REST endpoints
• Security Integration: method authorization enforced on every request, per-tool/resource/prompt RBAC (allowed-roles), shadow/dry-run (_dryRun), response shaping, per-tool rate limiting, and a tool-description hygiene scanner
• Tool Discovery: automatic tool discovery, pagination, resource templates (flapi://customers/{id}), and x-mcp-header for per-tenant edge routing

See docs/MCP_REFERENCE.md — the dual-era model and all new capabilities are documented in §11.

MCP Endpoints

• POST /mcp/jsonrpc - Main JSON-RPC endpoint for tool calls
• GET /mcp/health - Health check endpoint

Unified Configuration

MCP is now automatically enabled - no separate configuration needed! Every flAPI instance runs both REST API and MCP servers concurrently.

Configuration files can define multiple entity types:

REST Endpoint + MCP Tool (Unified)

# Single configuration file serves as BOTH REST endpoint AND MCP tool
url-path: /customers/                    # Makes this a REST endpoint
mcp-tool:                                # Also makes this an MCP tool
  name: get_customers
  description: Retrieve customer information by ID
  result-mime-type: application/json

request:
  - field-name: id
    field-in: query
    description: Customer ID
    required: false
    validators:
      - type: int
        min: 1
        max: 1000000
        preventSqlInjection: true

template-source: customers.sql
connection: [customers-parquet]

rate-limit:
  enabled: true
  max: 100
  interval: 60

auth:
  enabled: true
  type: basic
  users:
    - username: admin
      password: secret
      roles: [admin]

MCP Resource Only

# MCP Resource example
mcp-resource:
  name: customer_schema
  description: Customer database schema definition
  mime-type: application/json

template-source: customer-schema.sql
connection: [customers-parquet]

Using MCP Tools

Once MCP is enabled, you can interact with tools using JSON-RPC 2.0:

# Check MCP server health
curl 'http://localhost:8081/mcp/health'

# Initialize MCP connection
curl -X POST http://localhost:8081/mcp/jsonrpc \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "initialize"}'

# List available tools (discovered from unified configuration)
curl -X POST http://localhost:8081/mcp/jsonrpc \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 2, "method": "tools/list"}'

# Call a tool (same SQL template used for both REST and MCP)
curl -X POST http://localhost:8081/mcp/jsonrpc \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": {"name": "get_customers", "arguments": {"id": "123"}}}'

🎓 Learn by doing

The guides below are task-shaped — each one gets you to a working result, then links to the reference and to the implementation notes.

I want to…	Guide
Serve my first endpoint	Getting started
Add validation, filters, pagination	Building a REST endpoint
Let an AI agent call it	Exposing an endpoint as an MCP tool
Make a slow query fast	Caching an expensive query
Require a token, restrict by role	Requiring authentication
Read from S3, GCS or Azure	Reading from cloud storage
Stop copy-pasting config blocks	Reusing config and reading the environment
Deploy one self-contained binary	Shipping one self-contained binary
Trace requests and keep an audit trail	Observability

🏭 Building from source

The source code of flAPI is written in C++ and closely resembles the DuckDB build process. A good documentation of the build process is the GitHub action in build.yaml. In essecence a few prerequisites need to be met: In essecence a few prerequisites need to be met:

• Install the dependencies: sudo apt-get install -y build-essential cmake ninja-build
• Checkout the repository and submodules: git clone --recurse-submodules https://github.com/datazoode/flapi.git
• Build the project: make release

The build process will download and build DuckDB v1.5.5 and install the vcpkg package manager. We depend on the following vcpkg ports:

• argparse - Command line argument parser
• crow - Our REST-Web framework and JSON handling
• yaml-cpp - YAML parser
• jwt-cpp - JSON Web Token library
• openssl - Crypto library
• catch2 - Testing framework

Note: MCP support is built-in and doesn't require additional dependencies beyond what's already included.

📚 Documentation

Start at the documentation index — it routes you from what you are trying to do to the right page.

• Guides — task-first walkthroughs
• Configuration Reference — every flapi.yaml and endpoint key
• CLI Reference — server flags and subcommands
• MCP Reference — the MCP protocol as flAPI implements it
• Config Service API — runtime configuration over REST
• Observability — tracing, audit and log correlation
• Architecture & design — how it works inside, and why

MCP Registry

flAPI is listed in the official MCP Registry under the name below (this line also serves as the registry's PyPI ownership marker):

mcp-name: io.github.datazoode/flapi

📊 Telemetry

flAPI sends anonymous application_start and application_stop events to help the team understand adoption. No query data, credentials, or personal information is ever sent.

Opt out (any one of these is sufficient):

# One-off via CLI flag
./flapi --no-telemetry

# Per-session via environment variable
export FLAPI_NO_TELEMETRY=1
./flapi

# Permanently via config file (flapi.yaml)
telemetry:
  enabled: false

See CLI Reference and Configuration Reference for full details.

🤝 Contributing

We welcome contributions. Open an issue to discuss a change, or send a pull request.

📄 License

flAPI is licensed under the Business Source License (BSL) Version 1.1. The BSL is a source-available license that gives you the following permissions:

Allowed

1. Copy, modify, and create derivative works: You can copy the software, modify it, and create derivative works.
2. Redistribute and non-production use: Redistribution and non-production use of the software is permitted.
3. Limited production use: You can use flAPI in production, but with one restriction (see below).
4. Change License rights: After the Change Date (five years from first publication of the Licensed Work), the software automatically becomes available under the Change License (MPL 2.0).

Not allowed

1. Offering to third parties on a hosted or embedded basis: The Additional Use Grant explicitly restricts using the software in a way that offers it to third parties as a hosted service or embedded component. If you want to do that, you need a commercial license.
2. Violation of current license requirements: If your use does not comply with the BSL, you must either purchase a commercial license or stop using flAPI.
3. Trademark usage: You do not have rights to the flAPI or DataZoo trademarks or logos, except as expressly required by the License.

For commercial licensing — embedding flAPI in a product, offering it as a hosted service, or any redistribution that the Additional Use Grant restricts — contact contact@data-zoo.de.

Licensing parameters

• Licensor: DataZoo GmbH
• Licensed Work: flAPI — SQL-to-API framework
• Change Date: five years from the first publication of each version
• Change License: MPL 2.0

See the LICENSE file for the full text.

🙋‍♀️ Support

If you have any questions or need help, please open an issue.

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Feedback

If flAPI misbehaves — an endpoint that will not serve, a cache that will not invalidate, an auth flow that will not complete — please open an issue. Deployments differ in ways we cannot reproduce here, so a report with your config is the fastest path to a fix. Every JSON error response carries a report_issue link for exactly this reason.

If it saved you time, a star on the repo helps other people find it.

On an interactive start, a small banner says the same thing once a day. Under a container or systemd there is no terminal, so it never prints — the startup log line carries the pointer instead. Silence both with DATAZOO_NO_BANNER=1.