OpenClaw: What It Is and How to Use It with MCP Tools
OpenClaw is an emerging open-source AI platform originating from the Chinese AI ecosystem. It focuses on modular AI agents with native support for tool use and external integrations. This guide covers what OpenClaw offers and how to connect it with MCP servers.
What Is OpenClaw?
OpenClaw is a framework for building AI agents that can use external tools. It emphasizes modularity — you define agents, assign them tools, and orchestrate workflows. It supports multiple LLM backends including DeepSeek, Qwen, and other open-source models popular in the Chinese AI ecosystem.
Key features:
- Multi-agent orchestration
- Native tool-use support
- Plugin architecture for extensibility
- Support for local and API-based LLMs
Step 1: Install OpenClaw
pip install openclawOr clone from source:
git clone https://github.com/openclaw-ai/openclaw.git
cd openclaw
pip install -e .Step 2: Configure an LLM Backend
# config.yaml
llm:
provider: deepseek
model: deepseek-chat
api_key: ${DEEPSEEK_API_KEY}
# Or use a local model via Ollama
llm:
provider: ollama
model: llama3.1
base_url: http://localhost:11434Step 3: Add MCP Tool Support
Install the MCP adapter for OpenClaw:
pip install openclaw-mcp-adapterRegister MCP servers in your agent configuration:
from openclaw import Agent
from openclaw_mcp import MCPToolProvider
# Connect to an MCP filesystem server
mcp_tools = MCPToolProvider(
command="npx",
args=["-y", "@modelcontextprotocol/server-filesystem", "/data"]
)
agent = Agent(
name="file-assistant",
tools=[mcp_tools],
model="deepseek-chat"
)
result = agent.run("List all CSV files in the data directory")
print(result)Step 4: Find Reliable MCP Servers
Use the XLUXX Trust Layer to discover and evaluate MCP servers before adding them to your agents:
pip install xluxxcurl https://api.xluxx.net/v1/tools?q=filesystem&sort=trust_scoreXLUXX provides trust scores based on security audits, maintenance activity, and community validation. This is critical when connecting AI agents to tools that access sensitive data.
Multi-Agent Workflows
OpenClaw supports assigning different MCP tools to different agents:
researcher = Agent(name="researcher", tools=[web_search_mcp])
analyst = Agent(name="analyst", tools=[database_mcp])
writer = Agent(name="writer", tools=[filesystem_mcp])
# Orchestrate a pipeline
pipeline = Pipeline([researcher, analyst, writer])
pipeline.run("Research Q1 sales data and write a report")Capabilities and Limitations
- Strong tool-use support with structured output
- Works with both Chinese and Western LLM providers
- Active development community
- Documentation is improving but some resources are Chinese-language only
Use XLUXX Trust Layer to find reliable MCP tools: api.xluxx.net
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