NetLogo-MCP
Enables teams to build and validate complex system simulations through conversational AI, reducing engineering time for modeling scenarios like resource allo…
The first MCP (Model Context Protocol) server for NetLogo, enabling AI assistants like Claude to create, run, and analyze agent-based models through natural conversation.
- Ask Claude to build a supply chain simulation model showing how inventory delays affect delivery times.
- Generate agent-based models to test different team communication strategies and measure productivity outcomes.
- Analyze simulation results from a crowd behavior model to identify bottlenecks in system design.
Enables teams to build and validate complex system simulations through conversational AI, reducing engineering time for modeling scenarios like resource allocation, network behavior, or supply chain dynamics without manual coding expertise.
Data scientists and operations engineers prototyping multi-agent simulations and emergent system behavior analysis.
https://github.com/Razee4315/NetLogo-MCP
By Razee4315
How to Get It
claude mcp add NetLogo-MCP -- npx -y NetLogo-MCP
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Build a supply chain simulation model showing how inventory delays affect delivery times
Trust Signals Auto-scanned
Data & Access
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 25 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 25 GitHub stars; contributors unknown; last commit 0d ago; license MIT.