Dive
Enables teams to extend LLM capabilities with custom tools and integrations without rebuilding infrastructure, reducing time-to-productivity for AI-assisted …
Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. ✨
- Ask Claude to analyze code repositories and identify performance bottlenecks automatically.
- Generate automated test cases from existing code using Claude's understanding of your codebase.
- Find bugs and security vulnerabilities by having Claude review your entire project structure.
Enables teams to extend LLM capabilities with custom tools and integrations without rebuilding infrastructure, reducing time-to-productivity for AI-assisted development workflows.
Engineering teams adopting LLM-based coding assistants who need local tool extensibility without vendor lock-in.
https://github.com/OpenAgentPlatform/Dive
By OpenAgentPlatform
How to Get It
claude plugins install OpenAgentPlatform/Dive
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.
After installing, paste this into Claude:
Analyze code repositories and identify performance bottlenecks automatically
Trust Signals Auto-scanned
Community Pulse New
- Show HN: Dive AI Agent, an Open Source MCP Client and Host for Desktop — Hacker News · 3 pts
- Show HN: Dive v0.8.0 Is Here – Major Architecture Overhaul and Feature Upgrades — Hacker News · 2 pts
- Show HN: V0.7.3 Update: Dive, an Open Source MCP Agent Desktop — Hacker News · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 1,804 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.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 1,804 GitHub stars; contributors unknown; last commit 0d ago; license MIT.