Fennara: Godot Plugin + MCP for AI Agents with Iterative Feedback Loop
Fennara is a dual-mode tool for Godot: it runs as an in-editor plugin and as an MCP (Model Context Protocol) server, meaning it can integrate with AI coding agents like Codex, Cursor, and Claude Code. The key differentiator isn't just that AI can execute Godot commands — it's the feedback loop after each command.
When an agent edits a script or scene, Fennara returns:
- Script diagnostics
- Scene validation results
- Runtime errors
- Node hierarchy and inspector data
- Screenshots of the current editor view
- Semantic search results against your project
This allows the agent to iteratively patch and rerun logic instead of guessing blindly. The creator demonstrated a workflow where a concept image is fed to Codex, which then uses the Fennara MCP to reconstruct the scene in Godot, iterating on visual and gameplay feedback.
Fennara is available at fennara.io.
Who It's For
Godot developers experimenting with AI-assisted coding who want more than one-shot command execution — specifically, those who want real-time feedback and correction cycles inside the editor.
📖 Read the full source: r/ClaudeAI
👀 See Also
ToolLoop: Open-Source Framework for Claude-Style Tools with Any LLM
ToolLoop is an open-source Python framework with 11 tools for file operations, code search, shell access, and sub-agents that works with any LLM through LiteLLM. The 2,700-line framework allows switching models mid-conversation while maintaining shared context.
Introducing Lean Collab: A Multi-Agent Orchestrator for Long-Running LLM Tasks
Lean Collab is an open-source orchestrator designed to manage long-running LLM tasks using coordinated, parallel sub-agents.
Security scanning skill for AI coding agents checks deployments automatically
A developer created a skill file that enables AI coding agents to automatically scan their own deployments for exposed .env files, open ports, missing security headers, and leaked source code. The scan runs after every deploy and takes about 30 seconds.
Measuring Off-Task Token Spend in Claude Code: The 'Undeclared-Intent' Metric
A developer built a metric to quantify compute spent on unintended execution paths in Claude Code sessions, finding that 22.8% of tokens went to off-task work.