pymnifocus
A Python toolkit for OmniFocus on macOS: an MCP server for AI assistant integration (Cursor, Claude, Gemini) and a standalone CLI query tool.
Inspired by themotionmachine/OmniFocus-MCP, rebuilt in Python with security hardening, a CLI, and PyPI packaging.
Prerequisites
- macOS with OmniFocus installed and running
- Python 3.10+
- uv (recommended) or pip
Installation
# From PyPI
pip install pymnifocus
# Or with uv
uv pip install pymnifocus
# For development
git clone https://github.com/vdanen/pymnifocus.git
cd pymnifocus
uv sync
CLI Query Tool
Query OmniFocus directly from your terminal with pymnifocus-query:
# Shorthand flags
pymnifocus-query --overdue
pymnifocus-query --flagged --sort dueDate
pymnifocus-query --inbox
pymnifocus-query --due-within 7 --limit 10
pymnifocus-query --project "Weekly Review"
pymnifocus-query --tag work --tag urgent
pymnifocus-query --available --summary
pymnifocus-query --today
# JSON input (same format as MCP query_omnifocus tool)
pymnifocus-query '{"entity": "tasks", "filters": {"status": ["Overdue", "DueSoon"]}, "sortBy": "dueDate"}'
# Pipe from stdin
echo '{"entity": "projects", "filters": {"status": ["Active"]}}' | pymnifocus-query
# Other tools
pymnifocus-query --tags
pymnifocus-query --perspectives
pymnifocus-query --dump
# Raw JSON output (for scripting)
pymnifocus-query --overdue --json
Run pymnifocus-query --help for full usage.
MCP Server
The MCP server enables AI assistants to interact with OmniFocus through natural language.
Running the Server
# Stdio transport (default, for Cursor/Claude/Gemini)
pymnifocus-server
# Streamable HTTP transport (for web clients or container access)
pymnifocus-server --transport streamable-http
pymnifocus-server --transport streamable-http --port 9000
# Or via module
python -m pymnifocus
Run pymnifocus-server --help for all options.
Cursor Integration
Edit ~/.cursor/mcp.json:
{
"mcpServers": {
"omnifocus": {
"command": "pymnifocus-server"
}
}
}
Or if using uv from a local clone:
{
"mcpServers": {
"omnifocus": {
"command": "uv",
"args": [
"run",
"--project",
"/path/to/pymnifocus",
"python",
"-m",
"pymnifocus.server"
]
}
}
}
Restart Cursor or reload MCP servers (Cmd+Shift+P -> "MCP: Restart Servers").
Claude Code Integration
Same as above, just edit ~/.claude.json.
Claude Desktop Integration
Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"omnifocus": {
"command": "pymnifocus-server"
}
}
}
Restart Claude Desktop.
Google AI Studio / Gemini
For MCP-compatible Gemini clients, the server uses stdio transport by default:
- Command:
pymnifocus-server
For Streamable HTTP (web-based clients):
pymnifocus-server --transport streamable-http
Then connect to http://localhost:8000/mcp.
Container Access
Since OmniFocus is a macOS application, the MCP server must run on the macOS host. Containers can reach it over HTTP using host.docker.internal.
1. Start the server on the host:
pymnifocus-server --transport streamable-http
This binds to 127.0.0.1:8000 by default. OmniFocus must be running.
2. Configure the MCP client inside the container:
{
"mcpServers": {
"omnifocus": {
"url": "http://host.docker.internal:8000/mcp"
}
}
}
3. Sample Containerfile:
FROM python:3.13-slim
RUN pip install --no-cache-dir mcp-client-cli
# Configure MCP to reach the host's pymnifocus server
RUN mkdir -p /root/.config
COPY <<'EOF' /root/.config/mcp.json
{
"mcpServers": {
"omnifocus": {
"url": "http://host.docker.internal:8000/mcp"
}
}
}
EOF
CMD ["bash"]
Build and run (Docker or Podman):
docker build -t my-mcp-app .
docker run --rm -it my-mcp-app
Note:
host.docker.internalresolves to the host machine on Docker Desktop for Mac and Podman Machine. On Linux with native Docker, add--add-host=host.docker.internal:host-gatewayto thedocker runcommand.
Available MCP Tools
| Tool | Description |
|---|---|
query_omnifocus |
Query tasks, projects, or folders with filters |
dump_database |
Get full OmniFocus database state |
add_omnifocus_task |
Add a new task |
add_project |
Add a new project |
remove_item |
Remove a task or project |
edit_item |
Edit a task or project |
batch_add_items |
Add multiple items at once (max 100) |
batch_remove_items |
Remove multiple items at once (max 100) |
list_perspectives |
List available perspectives |
get_perspective_view |
Get items from a perspective |
list_tags |
List all tags with hierarchy |
MCP Resources
| URI | Description |
|---|---|
omnifocus://inbox |
Current inbox items |
omnifocus://today |
Today's agenda (due, planned, overdue) |
omnifocus://flagged |
All flagged items |
omnifocus://stats |
Database statistics |
omnifocus://project/{name} |
Tasks in a project |
omnifocus://perspective/{name} |
Items in a perspective |
Example Prompts
- "Show me all flagged tasks due this week"
- "Add a task 'Review quarterly report' to my Work project, due Friday"
- "What's in my inbox?"
- "List all my projects"
- "Create a project called 'Website Redesign' with 3 tasks"
How It Works
The server communicates with OmniFocus using:
- OmniJS scripts executed via JXA (
osascript -l JavaScript) for queries, dumps, perspectives, and tags - AppleScript for add/edit/remove operations
OmniFocus must be running for either the MCP server or the CLI tool to function.
Security
- All user input is validated and escaped before embedding in generated scripts
- Entity names, sort fields, and field names are whitelisted
- Numeric parameters are validated as integers
- AppleScript strings are sanitized against injection (quotes, backslashes, newlines)
- Script paths are constrained to prevent directory traversal
- Batch operations are capped at 100 items
- Query results are capped at 5000 items
- Stdio transport: all communication is local (no network traffic)
- HTTP transport: binds to localhost by default; use
--hostto override
License
MIT
Credits
Inspired by themotionmachine/OmniFocus-MCP. OmniJS scripts are adapted from that project.
Metadata
Release files for pymnifocus 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pymnifocus-0.1.2.tar.gz | 92.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pymnifocus-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 128.7 kB
Release files / pymnifocus-0.1.2.tar.gz
| Download URL | pymnifocus-0.1.2.tar.gz |
|---|---|
| Size | 92.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2d85f7f6d8a8564abcd966100085ebba7dce31eadd5b652a90e10b8b9ab88d6d
|
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
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PyPI Publish Attestation
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Signed by GitHub Actions, verified by PyPI on Jul 18, 2026.
Transparency logRelease files / pymnifocus-0.1.2-py3-none-any.whl
| Download URL | pymnifocus-0.1.2-py3-none-any.whl |
|---|---|
| Size | 36.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b503b7a5664cde3b04486007765b9e9b6dc072824a3064a6dbb0d2fb6a04dc62
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BLAKE2b-256 checksum How to use checksums |
4613e203f4c8ac499664d294eb53a17f2f7e39dbead292c701a9030223b1c2a6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 18, 2026.
Transparency log