ftrack MCP server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ftrack MCP serverfind all in-progress lighting shots"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ftrack MCP server
A Model Context Protocol server that gives LLM agents (Claude Desktop, Claude Code, Cursor, …) broad, typed access to the ftrack Studio production-tracking API.
Why another one? The only existing ftrack MCP on GitHub is an early, unlicensed single-author experiment. This one is MIT-licensed, broad-coverage, and tested live against an ftrack Studio trial.
Coverage philosophy
ftrack's API is a generic query + CRUD over a flexible schema (143 entity types). So coverage comes from two layers:
Generic power tools —
query,query_one,create,update,delete— reach every entity type via ftrack's query language. This alone is ~full coverage.Typed convenience tools — projects, structure, tasks, statuses, assignments, notes, lists, time logs, thumbnails, users, and full schema introspection — make the common production ops one call each.
28 tools in total (see below).
Related MCP server: Devici MCP Server
Install
pip install -r requirements.txt # fastmcp, ftrack-python-api, requestsConfigure (credentials)
Set three env vars (or pass them in your MCP client config):
var | value |
|
|
| your login email |
| a Personal API key — ftrack ▸ avatar ▸ My account ▸ Security settings ▸ Create API key |
Run
python3 server.py # stdio transportWire into Claude Code
claude mcp add ftrack \
-e FTRACK_SERVER=https://yourstudio.ftrackapp.com \
-e FTRACK_API_USER=you@studio.com \
-e FTRACK_API_KEY=*** \
-- python3 /abs/path/to/ftrack-mcp/server.py(Tools appear as mcp__ftrack__* on the next session.) For Claude Desktop / Cursor, add the same command +
env to the app's mcpServers config.
Tools
Generic (full reach): query · query_one · create · update · delete
Schema / discovery: list_entity_types · get_entity_schema · list_project_schemas · list_statuses ·
list_task_types · list_object_types · list_priorities · list_custom_attributes
Projects / structure: list_projects · get_project · create_project · list_children · list_tasks ·
create_task
Ops: set_status · assign_task · add_note · get_notes · list_lists · log_time · set_thumbnail
Users / meta: whoami · list_users
Examples (what an agent would call)
"every in-progress lighting shot" →
query("Task where type.name is \"Lighting\" and status.name is \"In progress\"", ["name","parent.name"])"add a note to this shot" →
add_note("Shot", "<id>", "Looks good, ship it")"what fields does a Shot have?" →
get_entity_schema("Shot")"make a task" →
create_task("<shot_id>", "Compositing", status="Ready to start")
Notes
Reads return entities serialized to the fields you request (dot-paths like
status.name), to avoid lazy-loading huge relations. Writes auto-resolve{"id": "..."}references (parent →Context, project →Project, status →Status, type →Type, …).Validated live: all 28 tools register; query/schema/create/update/delete/notes/status round-trips pass.
A TS port over the official
ftrack-javascriptSDK is straightforward if you want it in-stack.
MIT © 2026 John Huikku
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