SDK Agent Commands and MCP Tools#

The UAV Mission Compute SDK ships two complementary interfaces for AI agents:

Slash commands for Claude Code that wrap the most common stack lifecycle and validation workflows.
An MCP server that exposes Intel Edge AI tools (Anomalib, DLStreamer, Edge AI Suites) and live MAVLink telemetry to any MCP-capable agent.

Together they let you bring up the PX4 + Gazebo + OpenVINO stack, verify it, capture data, and drive higher-level AI workflows — all through natural language.

Claude Code Slash Commands#

The SDK repository provides ready-to-use slash commands under .claude/commands/. Once the repository is opened with Claude Code from federal-and-aerospace-ai-suite/uav-mission-compute-sdk/, the commands below are auto-discovered and invocable as /<command-name>.

Command	What it does	Typical usage
`/start-stack`	Brings up the full UAV infrastructure (mosquitto, mediamtx, PX4, companion bridge, camera bridge, observability). Supports `sim` (default 3-camera Gazebo) and `usb` (single USB camera) modes, plus `-lean` variants that skip Grafana/InfluxDB.	`/start-stack sim` or `/start-stack usb`
`/validate-infra`	Runs a health sweep across the stack: container status, MQTT broker connectivity, PX4 SITL process, companion bridge, MediaMTX API, RTSP camera streams, and telemetry flow. Camera-profile aware (checks only the active bridge).	`/validate-infra`
`/capture-camera`	Captures a single frame (or short clip) from any UAV camera for debugging. Prefers RTSP (`rtsp://localhost:8554/uav-1/<camera>`) and falls back to MQTT legacy mode. Arms the UAV first if needed.	`/capture-camera nadir`
`/switch-camera-mode`	Switches the running stack between simulated 3-camera mode (`nadir,forward,rear`) and real USB camera mode (`nadir`). Updates `.env`, tears down current profile, and brings up the target profile.	`/switch-camera-mode sim` or `/switch-camera-mode usb`
`/cleanup-stack`	Stops sample apps + helpers first, then core infra across both camera profiles, and runs `make clean`. Points at `make clean-all` for deeper cleanup (compose volumes + unused images).	`/cleanup-stack`

Typical Session#

/start-stack sim
/validate-infra
/capture-camera nadir
/switch-camera-mode usb
/cleanup-stack

Each command file is a self-contained runbook — the agent reads the file, prompts for any missing arguments (for example the target camera), executes the documented shell steps, and reports the outcome.

MCP Server — Edge AI Skills#

The SDK also includes a Model Context Protocol server at uav-mission-compute-sdk/mcp-server/ that exposes Intel Edge AI tooling and live MAVLink telemetry to any MCP-capable agent (Claude Code, GitHub Copilot with MCP, etc.).

Quick Start#

From uav-mission-compute-sdk/mcp-server/:

# Full setup (installs uv, clones supporting repos, configures MCP)
./setup.sh

# Or, for iterative development
make dev          # Install uv + dependencies
make verify       # Check tool discovery
make run          # Start the server

Then launch Claude Code from the workspace directory and the tools below become available. See the full MCP server README for custom workspace paths, production deployment, and the Docker recipe.

Exposed Tools#

The server groups tools by domain. Each tool is invoked by the agent when its description matches the user request.

Anomalib — Anomaly Detection#

Tool	Purpose
`anomalib_train`	Train anomaly detection models on a dataset
`anomalib_predict`	Run inference on images
`anomalib_export`	Export a trained model to OpenVINO / ONNX
`anomalib_benchmark`	Benchmark model performance
`anomalib_openvino_inference`	Run OpenVINO inference on exported models

DLStreamer — Video Analytics#

Tool	Purpose
`dlstreamer_build_pipeline`	Compose a video analytics pipeline (detection, tracking, classification)
`dlstreamer_run_sample`	Run a bundled sample application
`dlstreamer_list_samples`	List available sample pipelines
`dlstreamer_download_models`	Download pre-trained models

Edge AI Suites — Application Deployment#

Tool	Purpose
`edge_ai_suites_deploy_app`	Deploy a production Edge AI Suites application
`edge_ai_suites_list_apps`	List available applications across suites
`edge_ai_suites_sdk_install`	Install SDK components

MAVLink — Live UAV Telemetry#

Tool	Purpose
`mavlink_get_telemetry`	Get the full telemetry snapshot
`mavlink_get_position`	Get GPS position
`mavlink_get_attitude`	Get orientation (roll / pitch / yaw)
`mavlink_get_battery`	Get battery status
`mavlink_get_velocity`	Get velocity vector
`mavlink_get_status`	Get flight status
`mavlink_check_health`	Health check against the vehicle
`mavlink_monitor_flight`	Monitor a flight in real time
`mavlink_collect_flight_data`	Collect flight data logs

Example Prompts#

Train a defect detector on aerial inspection images in ./data.

Build an object tracking pipeline for the UAV nadir RTSP stream.

Deploy the worker safety monitoring app.

Monitor the current flight and alert me if battery drops below 20%.

When to Use Which#

Use the slash commands for stack lifecycle work — starting, validating, capturing from, switching, and tearing down the local UAV simulation.
Use the MCP server tools for higher-level AI workflows — training and exporting models, building analytics pipelines, deploying suite applications, and querying live vehicle telemetry.

Both can be used together in the same Claude Code session: bring the stack up with /start-stack sim, then ask the agent to build a DLStreamer pipeline against the running RTSP source.