dcc-mcp-core 0.12.24

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 (~140 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 14 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 & loading | SkillScanner, SkillCatalog, SkillWatcher, dependency resolver | | dcc-mcp-protocols | MCP protocol types | ToolDefinition, ResourceDefinition, PromptDefinition, DccAdapter, BridgeKind | | dcc-mcp-transport | IPC communication | TransportManager, ConnectionPool, IpcListener, FramedChannel, CircuitBreaker, FileRegistry | | 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-http | MCP Streamable HTTP server | McpHttpServer, McpHttpConfig, ServerHandle, Gateway (first-wins competition) | | dcc-mcp-server | Binary entry point | dcc-mcp-server CLI, gateway runner | | 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
• ~140 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 11 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 (~140 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