# An LLM-Agnostic, MAVLink-Based Drone Command and Control Interface and Agentic Harness Using the Model Context Protocol

arXiv: https://arxiv.org/abs/2601.15486 · HTML: https://mlsanity.com/paper/2601.15486/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 70 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 20 of 20)

- [Real-Time Anomaly Detection and Reactive Planning with Large Language Models](https://mlsanity.com/paper/2407.08735/) (2024)
- [AeroVerse: UAV-Agent Benchmark Suite for Simulating, Pre-training, Finetuning, and Evaluating Aerospace Embodied World Models](https://mlsanity.com/paper/2408.15511/) (2024)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios](https://mlsanity.com/paper/2511.11252/) (2025)
- [CHAI: Command Hijacking against embodied AI](https://mlsanity.com/paper/2510.00181/) (2025)
- [Next-Generation LLM for UAV: From Natural Language to Autonomous Flight](https://mlsanity.com/paper/2510.21739/) (2025)
- [α3-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.03281/) (2026)
- [General-Purpose Aerial Intelligent Agents Empowered by Large Language Models](https://mlsanity.com/paper/2503.08302/) (2025)
- [LogisticsVLN: Vision-Language Navigation For Low-Altitude Terminal Delivery Based on Agentic UAVs](https://mlsanity.com/paper/2505.03460/) (2025)
- [Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning](https://mlsanity.com/paper/2509.13352/) (2025)
- [LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems](https://mlsanity.com/paper/2501.11864/) (2025)
- [Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms](https://mlsanity.com/paper/2505.23576/) (2025)
- [Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent](https://mlsanity.com/paper/2506.07509/) (2025)
- [AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models](https://mlsanity.com/paper/2511.01472/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective](https://mlsanity.com/paper/2509.12795/) (2025)
- [Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence](https://mlsanity.com/paper/2507.15049/) (2025)
- [Robot builds a robot's brain: AI generated drone command and control station hosted in the sky](https://mlsanity.com/paper/2508.02962/) (2025)
- [Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning](https://mlsanity.com/paper/2508.07885/) (2025)
- [Chat with UAV -- Human-UAV Interaction Based on Large Language Models](https://mlsanity.com/paper/2512.08145/) (2025)

## Built on by (most cited first, 1 of 1)

- [RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control](https://mlsanity.com/paper/2607.17951/) (2026)

## Abstract (arXiv)

Artificial intelligence integrated with drone command and control (physical AI) offers a route to autonomy through large language models (LLMs), yet a unified LLM-to-drone interface has been missing. We present an LLM-agnostic command-and-control interface and agentic harness joining the Model Context Protocol (MCP) to MAVLink, the near-universal drone command-and-telemetry standard: "DroneServer" gives any MCP-capable LLM command, telemetry, mission, and safety functions over ArduPilot and PX4. Its 98 tools cover 223 of 238 client-side methods of MavSDK, MAVLink's library; 61 of the 98 were exercised in software-in-the-loop. We treat the LLM as an untrusted commander: zero public ports (externally verified); every command validated server-side (tiers, confirmation handshakes, independent geofence, audit log), exercised by an adversarial safety suite. Server-side mission state resolves the "fire-and-forget" mismatch: a scripted client's 37.8-minute mission survived a 4-minute disconnection. Over 1,000 simulated flights ran a ten-mission LLM-UAV control benchmark; eight of eleven models complete 90.0-100% of its six flying missions, and no aircraft left the permitted zone in 110 geofence-violation trials. On real hardware, the unmodified server commanded three quadcopters, GPS-guided and GPS-denied; five providers' models flew an unseen mission, ten of ten. The tests and datasets include: repeated trials, standardized evaluation tasks, success/failure statistics, latency analysis, uncertainty treatment, comparative benchmarking across models/platforms, prompt sets, mission complexity, tool-call traces, safety-intervention logs, and safety-performance assessment. The architecture is a scaffold for a higher-level drone-LLM operating system where plain-language commands realize complex missions, and a path to agent swarms commanding drone swarms under one security umbrella.