"task management" matching MCP servers:

Connects AI agents to MAVLink drones (PX4, ArduPilot) via a capability layer with safety guardrails, enabling telemetry, flight control, and mission management through natural language.

MCP server for controlling satellite ground station operations in the Trench simulation, including pass management, downlink control, and antenna pointing.

Enables LLMs and AI agents to interact with AFSIM through standardized MCP tools for scenario management, entity/component control, simulation execution, and results analysis.

Model Context Protocol server for pyCycle/OpenMDAO engine-cycle workflows, enabling lifecycle management, variable inspection, execution, sweeps, and integration with CPACS aircraft analysis pipelines.

This MCP server enables AI agents to control kOS computers in Kerbal Space Program, providing tools for scripting, telemetry, and simulation management via telnet and file system integration.

Enables AI assistants to control and automate Abaqus/CAE simulations via MCP, including model query, job management, ODB inspection, KPI extraction, capsule tracking, physics contract validation, report generation, and viewport capture. It also supports noGUI batch mode, dual transport, and integration with Codex/Claude clients.

Enables interaction with SU2 CFD solver for session lifecycle management, config editing, solver execution, and results inspection, with optional CPACS integration for aircraft analysis.

Enables checking US airports for ground stops, Ground Delay Programs, delays, and closures, while listing all active national airspace events and reading the Command Center's operations plan and full ATCSCC advisory text. It runs over stdio or Streamable HTTP and also decodes FAA event types, traffic-management terms, ARTCC codes, and identifier formats.

Provides MCP tools for CPACS-oriented TiGL workflows, enabling lifecycle management, inspection, export, and parameter manipulation of aircraft geometry models without native geometry runtimes.

SWMF MCP Server enables AI agents to work with the Space Weather Modeling Framework (SWMF) through tools for context, evidence, artifact inspection, and comparison. It includes task-specific skills for explanation, configuration, building, running, debugging, and analysis.