$ loading_
Let AI coding agents safely read, edit, search, and run local code.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Kontrol" yet — see the docs or source repo.
Connect to my local project, find the user login code, propose which files to change and the edits needed, and only apply them after I approve.
It first returns the relevant files and an edit plan, then applies controlled changes to local code after approval.
Search this project for all code that calls the payment API, list the file locations, and explain where error handling may be missing.
It outputs matching results, file locations, and issue notes to help quickly locate risky code areas.
After preparing the code changes, run the relevant commands to verify the code works and summarize the results and anything that needs my review.
It returns execution results, validation status, and the changes that require human review.
Developers can have AI read, search, and edit code in local projects while keeping a human review step. It fits teams that want faster code changes without giving full autonomy.
When teams need AI to run code or commands in local projects, policy controls and review loops help reduce risk. It is suitable for development environments that require safety and control.
It is a self-hosted MCP server that lets AI coding agents read, edit, search, and run code in local projects. It also emphasizes human review loops and policy controls.
The provided information only says it is self-hosted, which generally means users deploy and manage it themselves. For exact installation details, see the source repository.
It is designed for AI coding agents, not just for editing code. It also supports searching and running code, with human review and policy controls, making AI-assisted execution more governed.
Let AI see and control a computer for complex desktop automation.
Provide controlled repository access for AI coding agents with redaction and audit logs.
Give AI coding agents filesystem, Git, database, and compute tools via MCP.
Control multiple AI coding agents with session continuity and auth checks.
Provide AI agents with coding standards, testing, planning, and requirements guidance.
Enable AI coding agents to communicate, share state, and coordinate work in real time.