dcc-mcp-core

中文 | English

Rust-first control plane for connecting AI agents to live DCC sessions.

dcc-mcp-core turns Maya, Blender, Houdini, Photoshop, Godot, RenderDoc, and custom studio hosts into discoverable MCP and REST capabilities. It provides the gateway, skills, structured results, main-thread dispatch, diagnostics, IPC, workflows, and packaged CLI/server binaries needed to operate real desktop sessions.

Choose your entry point

The current contract

The default agent path is CLI + REST:

dcc-mcp-cli dcc-types (when adapter support is unclear)
  -> list
  -> search
  -> describe
  -> load-skill (only when needed)
  -> call

The gateway keeps tools/list bounded. It advertises the canonical discovery/dispatch wrappers (search, describe, load_skill, and call) instead of fanning every backend tool into one large list. Backend capabilities are discovered by search, inspected by describe, and invoked through the wrapper or the REST twin (POST /v1/search, /v1/describe, /v1/call).

When a concrete DCC session is needed, read gateway://instances; each entry already includes its mcp_url. Do not use the removed legacy instance tools (list_dcc_instances, get_dcc_instance, or connect_to_dcc). Wrapper inputs belong inside arguments; do not put backend fields beside tool_slug.

For direct per-DCC MCP connections, the compatibility discovery names (search_tools, describe_tool, and call_tool) remain available where the server exposes them. New gateway integrations should use the canonical names above.

Quick start: operate a DCC

dcc-mcp-cli is the preferred control path for every shell-capable agent. If it is missing, obtain the user's consent before installing the latest official release:

# Linux/macOS
curl -fsSL https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy Bypass -c "irm https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.ps1 | iex"

Keep an official build current through the release manifest:

dcc-mcp-cli update check
dcc-mcp-cli update apply

update apply downloads and stages the latest CLI for the next launch. It does not update a running dcc-mcp-server; update that server in its own environment.

Then discover a live capability before calling it:

dcc-mcp-cli dcc-types
dcc-mcp-cli list
dcc-mcp-cli search --query "create sphere" --dcc-type maya --limit 20
dcc-mcp-cli describe <tool-slug>
dcc-mcp-cli call <tool-slug> --json '{"radius": 2.0}' \
  --meta-json '{"agent_context":{"session_id":"task-42"}}'

dcc-types is an offline, catalog-backed capability query. It reports canonical adapter identifiers and install-plan availability; list remains the source of truth for live instances.

Replace the placeholder with the slug returned by search. For remote workstations, register a gateway profile and select it:

dcc-mcp-cli gateway register https://workstation.example:19293 --name pcA
dcc-mcp-cli gateway set pcA
dcc-mcp-cli list

Use dcc-mcp-cli doctor when startup or readiness is unclear. Open the local Admin UI at http://127.0.0.1:9765/admin after the gateway is available.

After a task is accepted, agents can inspect bounded gateway evidence with dcc-mcp-cli stats --range 24h --session-id task-42. A zero call count means no telemetry evidence, and direct local calls may not be represented. Feed the result plus short task and validation summaries to the review_skill_improvement prompt. The prompt defaults to no change, prefers improving an existing skill, and never grants authority to edit or publish outside the task scope.

Quick start: expose skills from Python

pip install dcc-mcp-core

Point the server at a skill directory and start the MCP endpoint:

import os

from dcc_mcp_core import McpHttpConfig, create_skill_server

os.environ["DCC_MCP_MAYA_SKILL_PATHS"] = "/path/to/skills"

server = create_skill_server("maya", McpHttpConfig())
handle = server.start()
print(handle.mcp_url())

Local DCC instances bind an OS-assigned port by default. The resolved URL is registered automatically, so the gateway and CLI discover it without a hardcoded per-instance port. Pass port=<number> only for an explicit fixed listener requirement.

For manual handler registration, use McpHttpServer and ToolRegistry. For adapter lifecycle, readiness, gateway registration, and hot reload, use DccServerBase as described in the adapter guides.

Add a skill without framework glue

The project follows the agentskills.io frontmatter contract. Put dcc-mcp-core extensions under metadata.dcc-mcp and keep tool declarations in a sibling file:

my-skill/
├── SKILL.md
├── tools.yaml
└── scripts/
    └── do_thing.py

# SKILL.md
---
name: my-skill
description: "Does a useful Maya task. Use when the user asks for it."
metadata:
  dcc-mcp:
    dcc: maya
    tools: tools.yaml
    search-hint: "geometry, scene task"
---

# tools.yaml
tools:
  - name: do_thing
    description: Do the task in the active Maya scene.
    source_file: scripts/do_thing.py
    execution: sync
    affinity: main
    annotations:
      read_only_hint: false
      destructive_hint: true

Run the same production validator used by CI with dcc-mcp-cli lint path/to/skills. See Skills for schemas, groups, dependencies, testing, and migration rules.

DCC UI Control

Desktop application automation for cases where native DCC APIs cannot observe or drive the interface state directly. Agents use ui_control__snapshot, ui_control__find, ui_control__act, ui_control__wait_for, and ui_control__stop_computer_use to observe, find, and act on target windows.

Capabilities

• Scoped window targeting — snapshots and actions are bound to a single process or window handle, never the whole desktop.
• Semantic UIA + raw input fallback — prefer stable semantic controls (button, text field, checkbox) resolved by ui_control__find, then fall back to screenshot-relative coordinates when custom-drawn controls have no semantic node.
• Bounded security model — every action is scoped by the adapter/operator-bound PID/HWND. Raw input requires an explicit opt-in (DCC_MCP_COMPUTER_USE_ALLOW_RAW_INPUT=true). Hard-denied: passwords, authentication controls, LockApp, Windows Security, terminals, and credential manager windows.
• Visible capsule overlay — while a native DCC UI Control session is active, click-through corner brackets mark the target window and a bottom-center capsule reads DCC UI Control · <app> | Esc to stop. The user stops control at any time with Esc.
• Audit trail — every snapshot, action, wait, stop, and rejected operation appends a redacted ui_control_operation event to the shared log directory, visible in the Admin Logs panel without exposing entered text or screenshot coordinates.

Tool reference

For detailed skill reference and agent workflows, see the ui-control skill.

Architecture

The runtime has four useful layers:

1. DCC service — owns skills and executes tools inside one DCC process.
2. Sidecar/supervisor — bridges host RPC, readiness, process lifetime, and gateway registration when an adapter uses the packaged runtime.
3. Gateway daemon — aggregates live instances and owns discovery, routing, REST, Admin UI, audit, and diagnostics.
4. Client surfaces — CLI, MCP clients, REST clients, and marketplace tools.

The Rust workspace and package membership are defined by the root Cargo.toml. The Python package is a PyO3 extension with pure Python helpers and supports Python 3.7–3.14. Build-from-source requirements come from rust-toolchain.toml and package metadata; the repository does not duplicate version or package counts in this README.

Documentation map

• Getting started — install the package and start a first server.
• CLI reference — complete operator commands and flags.
• Gateway guide — daemon, registry, routing, relay, and multi-instance behavior.
• REST API surface — request envelopes, tool_slug, readiness, and error contracts.
• Skills guide — authoring, loading, validation, and persistence.
• Adapter onboarding — the supported adapter implementation and release path.
• Documentation index — the complete guide/API map.
• AI agent guide and AGENTS.md — agent workflow and repository rules.

Development

Use the repository-pinned toolchain where possible:

vx just install
vx just dev
vx just test
vx just test-rust
vx just lint
vx just docs-check

docs-check builds the VitePress site and catches documentation links and syntax errors. Markdown lint runs in the docs CI workflow. See CONTRIBUTING.md for coding, testing, and release rules.

License

MIT — see LICENSE.