superwireless
Related Servers
Alternatives to superwireless
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityBmaintenanceExposes NVIDIA Sionna RT ray-tracing as 15 structured tools for AI agents, enabling wireless channel simulation (scene loading, antenna array setup, ray tracing, CSI extraction) through MCP.-
- AlicenseCqualityAmaintenanceMCP server that lets AI agents control a Rohde & Schwarz CMW500 radio communication tester via direct TCP/IP SCPI, providing 84 tools for LTE signaling, WLAN, Bluetooth/BLE, and GPRF measurements, with built-in safety limits and a simulator for evaluation.903AGPL 3.0
- AlicenseNot gradedqualityAmaintenanceMCP server enabling real-time communication, task delegation, and acknowledgment among multiple AI agents through a single local endpoint.55 npm4MIT
- AlicenseNot gradedqualityAmaintenanceEnables cloud LLM agents to discover and invoke physical hardware on edge and IoT devices through standard MCP tools, bridging constrained device channels like UART, BLE, and Wi-Fi.3MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to access unified development tools including code generation, documentation synchronization, test case rendering, and architecture graph queries through a single MCP server.-
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to investigate and manipulate live network simulations via MCP, including protocol debugging and fault injection.-
TDQS
Scored across 28 tools
Each tool targets a distinct resource/action, from presets to datasets to comparisons. Even the IoT-related tools (report, convert, design) have clear boundaries. Descriptions are detailed and effectively disambiguate similar-sounding tools like compare_scenarios, compare_arms, and compare_results.
All tools share the sw_ prefix and snake_case, with a consistent verb_noun pattern (e.g., list_presets, generate, validate, compare_arms). A few noun_phrases like mcs_info and sample_size are still readable and do not break the overall consistency.
28 tools exceeds the 25+ threshold, making the surface heavy. While the domain is broad, many tools could be grouped (e.g., the interference trio), and agents may struggle to select the right one from such a large set.
The full lifecycle is covered: planning (sw_plan, sw_revise), probing, generation, validation, evaluation, comparison, and reporting. External algorithm integration and preregistration complete the workflow. Minor features like dataset deletion are not necessary for the domain.