Benchmarking AI Agents for Hardware Design Automation via MCP Tool Calling

arXiv:2608.26199 · cs.AI, cs.SE · Submitted 2026-08-25 · Read on arXiv

cs.AI, cs.SE

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/signal-slot/mcp-gdb

Terminology

• Core — 45× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Model Context Protocol — 4× in this paper
• AI Agents — 3× in this paper · explained in 2 episodes
• Model Context Protocol (MCP) — 3× in this paper · explained in 8 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Agent Architectures — 2× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• gpt-oss-120b & gpt-oss-20b Model Card
• AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise
• ChipGPT: How far are we from natural language hardware design
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
• Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
• MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models
• The Llama 3 Herd of Models
• Does Prompt Formatting Have Any Impact on LLM Performance?
• MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
• $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
• ReAct: Synergizing Reasoning and Acting in Language Models
• SPRIG: Improving Large Language Model Performance by System Prompt Optimization

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