ki-manager — Knowledge Item MCP Server

AI-powered knowledge management for software projects.
Install once, use across all your projects.

What is ki-manager?

ki-manager is an MCP (Model Context Protocol) server that turns any project into a self-documenting codebase for AI agents (Claude, Antigravity, Cursor, Windsurf, etc.).

It provides:

• Knowledge Items (KI) — structured Markdown snapshots of each module, stored in .ki-base/knowledge/
• Coverage Audit — measures how well your KI base covers your actual code
• Dependency Analysis — auto-updates "Related KIs" by analyzing imports
• Git Snapshots — versioned knowledge state (git_checkpoint, git_restore)
• Scaffolding — one command creates the complete .ki-base/ structure in any project

Installation

Option A: uv tool install (recommended)

1. Install ki-manager globally via uv:

   uv tool install ki-manager
   

2. Add to your IDE MCP configuration (mcp_config.json):

   {
     "mcpServers": {
       "ki-manager": {
         "command": "ki-manager"
       }
     }
   }
   

   Note for Windows / ADI Antigravity: If ki-manager is not found in PATH by your IDE, specify the full path to the executable:

   • Windows: "C:\\Users\\<username>\\.local\\bin\\ki-manager.exe"
   • Linux/macOS: "/home/<username>/.local/bin/ki-manager"

Option B: pip / uv pip

pip install ki-manager
# or
uv pip install ki-manager

Option С: Local development

If you cloned the repository and are developing locally, point your IDE to the .venv Python directly:

{
  "mcpServers": {
    "ki-manager": {
      "command": "/absolute/path/to/repo/.venv/bin/python",
      "args": ["-m", "ki_manager.server"]
    }
  }
}

On Windows:

{
  "mcpServers": {
    "ki-manager": {
      "command": "C:\\path\\to\\repo\\.venv\\Scripts\\python.exe",
      "args": ["-m", "ki_manager.server"]
    }
  }
}

Quickstart

1. Add the MCP server to your IDE

Pick one of the options above and add it to your MCP config.

2. Initialize a project

In your IDE chat, call the ki_init_project tool:

ki_init_project(project_path="/absolute/path/to/your-project")

This creates:

your-project/
└── .ki-base/
    ├── config.json          ← machine-specific (auto-added to .gitignore)
    ├── ki_config.json       ← project settings (commit to git)
    ├── doc_config.json      ← file→KI map (commit to git)
    ├── AGENTS.md            ← agent instructions (commit to git)
    ├── DIR_INDEX.md         ← directory index (commit to git)
    └── knowledge/
        └── _OVERVIEW.ki.md  ← starter Knowledge Item

3. Start documenting

Use the available tools or slash commands:

Agent Skills

ki-manager distributes workflow instructions using the Agent Skills standard format.

Installing Skills

You can install the bundled workflow skills to your IDE's skills folder (e.g., for Google Antigravity):

# CD into your skills directory and run:
ki-manager-skills install-skills

# Or run via uvx specifying the target path explicitly:
uvx ki-manager-skills install-skills --path ~/.gemini/config/skills/

This will copy all workflow instructions (like create-adr, expand-knowledge) as standard .md skill files. It skips existing ones, so it's safe to run multiple times.

What Goes Into Git?

Security

The MCP server operates in a sandbox:

• All file access is restricted to the .ki-base/ directory
• Executable files (.py, .exe, .sh, etc.) cannot be modified via MCP
• Critical config files are protected from direct overwrite

Project Structure (this repo)

ki-manager/
├── pyproject.toml            ← pip / uvx package config
├── smithery.yaml             ← Smithery MCP marketplace config
├── src/ki_manager/
│   ├── server.py             ← MCP server entry point
│   ├── tools/
│   │   └── scaffold.py       ← ki_init_project implementation
│   └── scripts/              ← bundled analysis scripts
│       ├── ki_utils.py       ← shared utilities
│       ├── audit_coverage.py
│       ├── sync_agents_md.py
│       ├── generate_dir_index.py
│       ├── ki_dependency_analyzer.py
│       └── ...
├── knowledge/                ← KI documentation of this repo itself
└── decisions/                ← Architecture Decision Records

Troubleshooting

MCP server hangs on initialization (never connects)

Using uvx directly in mcpServers configuration is known to cause hangs or stdio pipe drops in ADI Antigravity and Windows environments due to ephemeral environment creation delays and process wrapping.

Solution: Install via uv tool install ki-manager and specify ki-manager or its absolute executable path in mcpServers.

Step 1 — Check logs:
Server logs are written to ~/.ki_base/logs/. Open the latest file and look for REQ: lines. If there are no REQ: lines at all, stdin is not being piped correctly by the IDE.

Step 2 — Specify exact executable path:
If your IDE cannot find ki-manager in system PATH:

{
  "mcpServers": {
    "ki-manager": {
      "command": "C:\\Users\\<username>\\.local\\bin\\ki-manager.exe"
    }
  }
}

Step 3 — Reinstall / Upgrade tool:
To upgrade to the latest PyPI version when using uv tool:

uv tool install --reinstall ki-manager

Server is running but no tools appear

Check that ki_status is visible in the IDE. If it shows No project active for current workspace, the server is working correctly — it just needs a project to be initialized (ki_init_project) or the IDE is not passing rootUri in the MCP handshake.

[Errno 22] Invalid argument in logs

This error was present in versions < 2.0.11 on Windows when wrapping sys.stdout with a codecs encoder. Upgrade to >= 2.0.11 to fix it:

uv tool install --reinstall ki-manager

License

MIT — free to use, copy, and adapt.