dcc-mcp-core 0.12.19

Foundational library for the DCC Model Context Protocol (MCP) ecosystem

dcc-mcp-core

中文文档 | English

Foundational library for the DCC Model Context Protocol (MCP) ecosystem. It provides a Rust-powered core with Python bindings (PyO3) that delivers high-performance skill management, skills discovery, transport, sandbox security, shared memory, screen capture, USD support, and telemetry — all with zero runtime Python dependencies. Supports Python 3.7–3.13.

Note: This project is in active development (v0.12+). APIs may evolve; see CHANGELOG.md for version history.

Why dcc-mcp-core?

Feature	Description
Performance	Rust core with zero-copy serialization via rmp-serde & LZ4 compression
Type Safety	Full PyO3 bindings with comprehensive .pyi type stubs (~120 public symbols)
Skills System	Zero-code script registration as MCP tools (SKILL.md + scripts/)
Resilient Transport	IPC with connection pooling, circuit breaker, retry policies
Process Management	Launch, monitor, auto-recover DCC processes
Sandbox Security	Policy-based access control with audit logging
Cross-Platform	Windows, macOS, Linux — tested on all three

AI-friendly docs: AGENTS.md | CLAUDE.md | GEMINI.md | .agents/skills/dcc-mcp-core/SKILL.md

Quick Start

Installation

# From PyPI (pre-built wheels for Python 3.7+)
pip install dcc-mcp-core

# Or from source (requires Rust toolchain)
git clone https://github.com/loonghao/dcc-mcp-core.git
cd dcc-mcp-core
pip install -e .

Basic Usage

import json
from dcc_mcp_core import (
    ActionRegistry, ActionDispatcher,
    EventBus, success_result, scan_and_load
)

# 1. Load skills; scan_and_load returns a 2-tuple (skills, skipped_dirs)
skills, skipped = scan_and_load(dcc_name="maya")
print(f"Loaded {len(skills)} skills")

# 2. Register skills from discovered skill packages
registry = ActionRegistry()
from pathlib import Path
for skill in skills:
    for script_path in skill.scripts:
        stem = Path(script_path).stem
        skill_name = f"{skill.name.replace('-', '_')}__{stem}"
        registry.register(name=skill_name, description=skill.description, dcc=skill.dcc)

# 3. Set up dispatcher and register a handler
dispatcher = ActionDispatcher(registry)
dispatcher.register_handler(
    "maya_geometry__create_sphere",
    lambda params: {"object_name": "pSphere1", "radius": params.get("radius", 1.0)},
)

# 4. Subscribe to lifecycle events
bus = EventBus()
bus.subscribe("action.after_execute", lambda **kw: print(f"event: {kw}"))

# 5. Dispatch a skill
result = dispatcher.dispatch(
    "maya_geometry__create_sphere",
    json.dumps({"radius": 2.0}),
)
output = result["output"]
print(f"Created: {output.get('object_name')}")

Core Concepts

ActionResultModel — Structured Results for AI

All skill execution results use ActionResultModel, designed to be AI-friendly with structured context and next-step suggestions:

from dcc_mcp_core import ActionResultModel, success_result, error_result

# Factory functions (recommended)
ok = success_result(
    "Sphere created",
    prompt="Consider adding materials or adjusting UVs",
    object_name="sphere1", position=[0, 1, 0]
)
# ok.context == {"object_name": "sphere1", "position": [0, 1, 0]}

err = error_result(
    "Failed to create sphere",
    "Radius must be positive"
)

# Direct construction
result = ActionResultModel(
    success=True,
    message="Operation completed",
    context={"key": "value"}
)

# Access fields
result.success      # bool
result.message     # str
result.prompt       # Optional[str] — AI next-step suggestion
result.error        # Optional[str] — error details
result.context      # dict — arbitrary structured data

ActionRegistry & Dispatcher — The Skill Execution System

import json
from dcc_mcp_core import (
    ActionRegistry, ActionDispatcher, ActionValidator,
    EventBus, SemVer, VersionedRegistry
)

# Registry with search support
registry = ActionRegistry()
registry.register("my_skill", description="My skill", category="tools", version="1.0.0")

# Validated dispatcher (takes only registry; validate separately with ActionValidator)
dispatcher = ActionDispatcher(registry)
dispatcher.register_handler("my_skill", lambda params: {"done": True})
result = dispatcher.dispatch("my_skill", json.dumps({}))
# result == {"action": "my_skill", "output": {"done": True}, "validation_skipped": True}

# Event-driven architecture
bus = EventBus()
sub_id = bus.subscribe("action.before_execute", lambda **kw: print(f"before: {kw}"))
bus.publish("action.before_execute", action_name="test")
bus.unsubscribe("action.before_execute", sub_id)

Skills System — Zero-Code MCP Tool Registration

The Skills system is dcc-mcp-core's most unique feature: it lets you register any script (Python, MEL, MaxScript, Batch, Shell, JS) as an MCP-discoverable tool with zero Python code. It reuses the OpenClaw Skills ecosystem format.

How It Works

SKILL.md (metadata) + scripts/ directory
       ↓  SkillScanner discovers & parses
SkillMetadata per skill (name, description, tags, script list)
       ↓  Skills registered in ActionRegistry → callable by AI via MCP

Quick Example

1. Create a Skill directory:

my-tool/
├── SKILL.md          # Metadata + description
└── scripts/
    └── list.py      # Your script

2. Write SKILL.md:

---
name: my-tool
description: "My custom DCC automation tools"
allowed-tools: ["Bash"]
tags: ["automation", "custom"]
dcc: maya
version: "1.0.0"
---
# My Tool

Automation scripts for Maya workflow optimization.

3. Add scripts/list.py

4. Set environment and use:

import os
os.environ["DCC_MCP_SKILL_PATHS"] = "/path/to/my-tool"

from dcc_mcp_core import scan_and_load, ActionRegistry

registry = ActionRegistry()
skills = scan_and_load(dcc_name="maya")
for s in skills:
    print(f"✓ {s.name}: {len(s.scripts)} scripts")

# Call a skill: {skill_name}__{script_name}
result = registry.call("my_tool__list", some_param="value")

Supported Script Types

Extension	Type	Execution
.py	Python	subprocess with system Python
.mel	MEL (Maya)	Via DCC adapter
.ms	MaxScript	Via DCC adapter
.bat, .cmd	Batch	cmd /c
.sh, .bash	Shell	bash
.ps1	PowerShell	powershell -File
.js, .jsx	JavaScript	node

See examples/skills/ for 11 complete examples: hello-world, maya-geometry, maya-pipeline, git-automation, ffmpeg-media, imagemagick-tools, usd-tools, clawhub-compat, multi-script, dcc-diagnostics, workflow.

Bundled Skills — Zero Configuration Required

dcc-mcp-core ships two core skills directly inside the wheel. They are available immediately after pip install dcc-mcp-core — no repository clone or DCC_MCP_SKILL_PATHS configuration needed.

Skill	Tools	Purpose
dcc-diagnostics	screenshot, audit_log, action_metrics, process_status	Observability & debugging for any DCC
workflow	run_chain	Multi-step action chaining with context propagation

from dcc_mcp_core import get_bundled_skills_dir, get_bundled_skill_paths

# Get the bundled skills directory (inside the installed wheel)
print(get_bundled_skills_dir())
# /path/to/site-packages/dcc_mcp_core/skills

# Returns [bundled_dir] or [] — ready to extend your search path
paths = get_bundled_skill_paths()                    # default ON
paths = get_bundled_skill_paths(include_bundled=False)  # opt-out

DCC adapters (e.g. dcc-mcp-maya) automatically include bundled skills by default. To opt-out: start_server(include_bundled=False).

Architecture Overview

dcc-mcp-core is organized as a Rust workspace of 11 crates, compiled into a single native Python extension (_core) via PyO3/maturin:

| Crate | Responsibility | Key Types | |----------------------|-----------| | dcc-mcp-models | Data models | ActionResultModel, SkillMetadata | | dcc-mcp-actions | Skill execution lifecycle | ActionRegistry, EventBus, ActionDispatcher, ActionValidator, ActionPipeline | | dcc-mcp-skills | Skills discovery | SkillScanner, SkillLoader, SkillWatcher, dependency resolver | | dcc-mcp-protocols | MCP protocol types | ToolDefinition, ResourceDefinition, PromptDefinition, DccAdapter types | | dcc-mcp-transport | IPC communication | TransportManager, ConnectionPool, IpcListener, FramedChannel, CircuitBreaker | | dcc-mcp-process | Process management | PyDccLauncher, ProcessMonitor, ProcessWatcher, CrashRecoveryPolicy | | dcc-mcp-sandbox | Security | SandboxPolicy, InputValidator, AuditLog | | dcc-mcp-shm | Shared memory | SharedBuffer, BufferPool, LZ4 compression | | dcc-mcp-capture | Screen capture | Capturer, cross-platform backends | | dcc-mcp-telemetry | Observability | TelemetryConfig, RecordingGuard, tracing | | dcc-mcp-usd | USD integration | UsdStage, UsdPrim, scene info bridge | | dcc-mcp-utils | Infrastructure | Filesystem helpers, type wrappers, constants, JSON |

Key Features

• Rust-powered performance: Zero-copy serialization (rmp-serde), LZ4 shared memory, lock-free data structures
• Zero runtime Python deps: Everything compiled into native extension
• Skills system: Zero-code MCP tool registration via SKILL.md + scripts/
• Validated dispatch: Input validation pipeline before execution
• Resilient IPC: Connection pooling, circuit breaker, automatic retry
• Process management: Launch, monitor, auto-recover DCC processes
• Sandbox security: Policy-based access control with audit logging
• Screen capture: Cross-platform DCC viewport capture for AI visual feedback
• USD integration: Universal Scene Description read/write bridge
• Structured telemetry: Tracing & recording for observability
• ~120 public Python symbols with full type stubs (.pyi)
• OpenClaw Skills compatible: Reuse existing ecosystem format

Installation

# From PyPI (pre-built wheels)
pip install dcc-mcp-core

# Or from source (requires Rust 1.85+)
git clone https://github.com/loonghao/dcc-mcp-core.git
cd dcc-mcp-core
pip install -e .

Development Setup

# Clone the repository
git clone https://github.com/loonghao/dcc-mcp-core.git
cd dcc-mcp-core

# Recommended: use vx (universal dev tool manager)
# Install vx: https://github.com/loonghao/vx
vx just install     # Install all project dependencies
vx just dev         # Build + install dev wheel
vx just test       # Run Python tests
vx just lint       # Full lint check (Rust + Python)

Without vx

# Manual setup
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install maturin pytest pytest-cov ruff mypy
maturin develop --features python-bindings,ext-module
pytest tests/ -v
ruff check python/ tests/ examples/
cargo clippy --workspace -- -D warnings

Running Tests

vx just test           # All Python tests
vx just test-rust       # All Rust unit tests
vx just test-cov        # With coverage report
vx just ci              # Full CI pipeline
vx just preflight       # Pre-commit checks only

Transport Layer — Inter-Process Communication

dcc-mcp-core provides a production-ready IPC transport layer:

from dcc_mcp_core import (
    TransportManager, TransportAddress, TransportScheme,
    RoutingStrategy, IpcListener, connect_ipc,
    FramedChannel
)

# Server side: listen for connections
listener = IpcListener.new("/tmp/dcc-mcp-server.sock")
handle = listener.start(handler_fn=my_message_handler)

# Client side: connect to server
channel = connect_ipc("/tmp/dcc-mcp-server.sock")
response = channel.call({"method": "ping", "params": {}})

# Advanced: connection pooling with resilience
mgr = TransportManager()
mgr.configure_pool(min_size=2, max_size=10)
mgr.set_circuit_breaker(threshold=5, reset_timeout=30)

Process Management — DCC Lifecycle Control

from dcc_mcp_core import (
    PyDccLauncher, PyProcessMonitor, PyProcessWatcher,
    PyCrashRecoveryPolicy
)

# Launch a DCC application
launcher = PyDccLauncher(dcc_type="maya", version="2025")
process = launcher.launch(
    script_path="/path/to/startup.py",
    working_dir="/project",
    env_vars={"MAYA_RENDER_THREADS": "4"}
)

# Monitor health
monitor = PyProcessMonitor()
monitor.track(process)
stats = monitor.stats(process)  # CPU, memory, uptime

# Auto-restart on crash
watcher = PyProcessWatcher(
    recovery_policy=PyCrashRecoveryPolicy(max_restarts=3, cooldown_sec=10)
)
watcher.watch(process)

Sandbox Security — Policy-Based Access Control

from dcc_mcp_core import SandboxContext, SandboxPolicy, InputValidator, AuditLog

# Define what's allowed
policy = (
    SandboxPolicy.builder()
    .allow_read(["/safe/paths/*"])
    .allow_write(["/temp/*"])
    .deny_pattern(["*.critical"])
    .require_approval_for("delete_*")
    .build()
)

ctx = SandboxContext(policy=policy)
validator = InputValidator(ctx)

# Validate before execution
if not validator.validate_action("delete_all_files"):
    print("Blocked by policy!")
else:
    print("Allowed — executing...")

# Review audit trail
audit = AuditLog.load()
for entry in audit.entries:
    print(f"{entry.timestamp} [{entry.action}] {entry.decision} → {entry.details}")

More Examples

See the examples/skills/ directory for 9 complete skill packages, and the VitePress docs site for comprehensive guides per module.

Release Process

This project uses Release Please to automate versioning and releases. The workflow is:

1. Develop: Create a branch from main, make changes using Conventional Commits
2. Merge: Open a PR and merge to main
3. Release PR: Release Please automatically creates/updates a release PR that bumps the version and updates CHANGELOG.md
4. Publish: When the release PR is merged, a GitHub Release is created and the package is published to PyPI

Commit Message Format

This project follows Conventional Commits:

Prefix	Description	Version Bump
feat:	New feature	Minor (0.x.0)
fix:	Bug fix	Patch (0.0.x)
feat!: or BREAKING CHANGE:	Breaking change	Major (x.0.0)
docs:	Documentation only	No release
chore:	Maintenance	No release
ci:	CI/CD changes	No release
refactor:	Code refactoring	No release
test:	Adding tests	No release

Examples

# Feature (bumps minor version)
git commit -m "feat: add batch skill execution support"

# Bug fix (bumps patch version)
git commit -m "fix: resolve middleware chain ordering issue"

# Breaking change (bumps major version)
git commit -m "feat!: redesign skill registry API"

# Scoped commit
git commit -m "feat(skills): add PowerShell script support"

# No release trigger
git commit -m "docs: update API reference"
git commit -m "ci: add Python 3.14 to test matrix"

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Workflow

1. Fork the repository and clone your fork
2. Create a feature branch: git checkout -b feat/my-feature
3. Make your changes following the coding standards below
4. Run tests and linting:

   vx just lint       # Check code style
   vx just test       # Run tests
   vx just prek-all   # Run all pre-commit hooks
   

5. Commit using Conventional Commits format
6. Push and open a Pull Request against main

Coding Standards

• Style: Code is formatted with ruff and isort (line length: 120)
• Type hints: All public APIs must have type annotations
• Docstrings: Google-style docstrings for all public modules, classes, and functions
• Testing: New features must include tests; maintain or improve coverage
• Imports: Use section headers (Import built-in modules, Import third-party modules, Import local modules)

License

This project is licensed under the MIT License - see the LICENSE file for details.

AI Agent Resources

If you're an AI coding agent, also see:

• AGENTS.md — Comprehensive guide for all AI agents (architecture, commands, API reference, pitfalls)
• CLAUDE.md — Claude-specific instructions and workflows
• GEMINI.md — Gemini-specific instructions and workflows
• .agents/skills/dcc-mcp-core/SKILL.md — Complete API skill definition for learning and using this library
• python/dcc_mcp_core/init.py — Full public API surface (~120 symbols)
• llms.txt — Concise API reference optimized for LLMs
• llms-full.txt — Complete API reference optimized for LLMs
• CONTRIBUTING.md — Development workflow and coding standards