SDL-MCP gives coding agents the right code context, not your entire repo. It turns sprawling codebases into compact,...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.
Copy the install, test the workflow, then decide if it earns a permanent slot.
Reasonable to try, but it will take more than a quick skim to get real signal.
GitHub health 42/100. no security policy. 3 open issues make this testable, but not something to trust blind.
AI Agent
Universal
Model
Multiple
Build Time
Instant
Fastest way to find out if sdl-mcp belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
git clone https://github.com/GlitterKill/sdl-mcp ~/.claude/agents/sdl-mcpRun this first. You will know quickly if the workflow earns a permanent slot.
rm -rf ~/.claude/agents/sdl-mcpNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ agents/ │ └─ sdl-mcp/ ← installs here └─ settings.json
SDL-MCP gives coding agents the right code context, not your entire repo. It turns sprawling codebases into compact, high-signal context that saves tokens, speeds up workflows, and improves agent output.