DEV Community

Cover image for How OpenClaw Implements MCP for Multi-Agent Orchestration
Olivier Buitelaar
Olivier Buitelaar

Posted on

How OpenClaw Implements MCP for Multi-Agent Orchestration

#webdev #programming #mcp #ai

How OpenClaw Implements MCP for Multi-Agent Orchestration

When you search for MCP orchestration today, the top results are Google ADK and Dynatrace — both solid tools for their respective niches. But they tell only part of the story. Google ADK is purpose-built for Google Cloud environments. Dynatrace approaches MCP from an observability angle. Neither gives you a self-hosted, multi-channel, multi-agent orchestration framework that treats MCP servers as native first-class tools with zero glue code.

That's what OpenClaw is.

OpenClaw is an open-source AI agent framework built around the idea that Model Context Protocol tools should just work — inside any agent, across any channel (Telegram, Discord, WhatsApp, Slack), without custom integration code. This guide walks through exactly how OpenClaw implements multi-agent MCP: the architecture, the practical setup, and how it compares to the alternatives.

What Is MCP (and Why It Matters for Multi-Agent Systems)?

Model Context Protocol (MCP) is an open standard that defines how AI agents discover and call external tools and data sources. It was introduced to solve the fragmentation problem: every tool integration used to require bespoke code. MCP standardises that surface.

MCP has three core primitives:

  • Tools — callable functions that agents invoke to take action (search the web, send an email, run a query)
  • Resources — structured data exposed by the server (files, database records, API responses)
  • Prompts — reusable prompt templates with parameterised inputs

For a multi-agent system, these primitives matter because they establish a shared tool interface. When every capability is described via MCP's self-describing schemas, agents can discover what's available, understand input/output contracts, and call tools without hardcoded bindings. You get plug-and-play capability composition across agents.

The problem is that most implementations stop here — they wire up a single agent to a single MCP server. Production multi-agent MCP orchestration requires more: routing tools to the right agents, managing sessions, controlling access, and coordinating across channels. That's the gap OpenClaw fills.

OpenClaw's MCP Architecture: Three Levels

OpenClaw integrates MCP at three distinct levels, each serving a different use case. Together they cover the full spectrum from broad app integrations to targeted browser automation.

Level 1: Plugin-Level MCP Clients

The primary integration point is OpenClaw's plugin system. Plugins are npm packages that connect to MCP servers at startup and register their tools as native agent tools inside the OpenClaw gateway. From the agent's perspective, an MCP tool looks identical to a built-in tool — no distinction, no extra configuration per call.

The Composio plugin is the canonical example. It connects to https://connect.composio.dev/mcp and registers 500+ app integrations — Gmail, Slack, GitHub, Notion, Google Workspace, and more — directly into the agent tool registry.

Plugin configuration lives in openclaw.json under plugins.entries:

{
  "plugins": {
    "entries": [
      {
        "package": "@composio/openclaw-plugin",
        "config": {
          "apiKey": "${COMPOSIO_API_KEY}",
          "mcpEndpoint": "https://connect.composio.dev/mcp"
        }
      }
    ]
  }
}
Enter fullscreen mode Exit fullscreen mode

Notice the ${COMPOSIO_API_KEY} syntax — OpenClaw interpolates environment variables directly in config, keeping secrets out of version control.

When the gateway starts, the plugin connects to the MCP server, fetches the tool manifest, and registers each tool. Agents can then call GMAIL_SEND_EMAIL or GITHUB_CREATE_ISSUE exactly as they would any native capability.

Level 2: Skill-Level MCP via mcporter

Not every MCP integration warrants a full plugin. For direct, CLI-level access to any MCP server, OpenClaw includes mcporter — a built-in skill that exposes MCP servers as command-line tools.

Core mcporter commands:

# List all tools available on a connected MCP server
mcporter list

# Call a specific tool with key=value arguments
mcporter call server.tool_name key=value key2=value2
Enter fullscreen mode Exit fullscreen mode

mcporter supports all three MCP transport protocols:

  • Stdio — for local MCP servers spawned as subprocesses
  • SSE (Server-Sent Events) — for remote HTTP-based MCP servers
  • WebSocket — for persistent bidirectional connections

This makes mcporter the right tool when you want to test an MCP server before writing a plugin, script one-off MCP calls, or integrate a niche server that doesn't have a dedicated OpenClaw plugin yet.

Level 3: Browser MCP Integration

OpenClaw ships with Chrome DevTools MCP integration for browser automation. Activate it with:

openclaw browser --mode user
Enter fullscreen mode Exit fullscreen mode

This connects the Chrome DevTools Protocol via MCP, giving agents the ability to automate browser interactions — navigation, form filling, screenshot capture, DOM inspection — through the same MCP tool interface used everywhere else.

Practical Walkthrough: Adding MCP Tools to Your Agents

Here's the end-to-end flow for wiring a new MCP server into your OpenClaw setup.

Step 1: Install the Plugin 

openclaw plugins install @scope/your-mcp-plugin
Enter fullscreen mode Exit fullscreen mode

OpenClaw validates the plugin manifest and runs install with --ignore-scripts by default for security. You can override this explicitly if needed, but the safe default prevents supply-chain script injection.

Step 2: Configure in openclaw.json

Add the plugin entry with any required config. Use ${ENV_VAR} for secrets:

{
  "plugins": {
    "entries": [
      {
        "package": "@scope/your-mcp-plugin",
        "config": {
          "endpoint": "https://your-mcp-server.example.com",
          "apiKey": "${YOUR_API_KEY}"
        }
      }
    ]
  }
}
Enter fullscreen mode Exit fullscreen mode

Step 3: Control Per-Agent Tool Access

This is where OpenClaw's multi-agent MCP orchestration shines. You don't have to expose every MCP tool to every agent. Use allowlists in agents.list[].tools.allow:

{
  "agents": {
    "list": [
      {
        "id": "content-agent",
        "tools": {
          "allow": ["GMAIL_SEND_EMAIL", "NOTION_CREATE_PAGE", "GITHUB_CREATE_ISSUE"]
        }
      },
      {
        "id": "ops-agent",
        "tools": {
          "allow": ["SLACK_SEND_MESSAGE", "GITHUB_LIST_ISSUES", "GITHUB_UPDATE_ISSUE"]
        }
      }
    ]
  }
}
Enter fullscreen mode Exit fullscreen mode

Each agent only sees — and can only call — the tools explicitly allowed for it. This is how you implement least-privilege access across a multi-agent system without per-agent plugin configuration.

Step 4: Use — Transparently

Once configured, agents call MCP tools exactly like any other capability. There's no special syntax, no MCP-specific client code in your agent logic. The OpenClaw gateway handles the MCP protocol layer entirely.

For direct CLI access, use mcporter:

mcporter call gmail.send_email to="team@example.com" subject="Deploy complete" body="v2.1.0 is live."
Enter fullscreen mode Exit fullscreen mode

OpenClaw vs Generic MCP Client

If you're evaluating whether to use OpenClaw or wire up your own MCP client directly, here's the honest comparison:

Capability Generic MCP Client OpenClaw
MCP tool execution Yes Yes
Multi-agent tool routing Manual Built-in allowlists
Persistent gateway daemon No Always-on session management
Multi-channel delivery No Telegram, Discord, WhatsApp, Slack
Hierarchical orchestration No Parent/child session model
Plugin security No Manifest validation + ignore-scripts
Scheduled MCP tool runs No Cron integration
Tool sandboxing No Configurable exec security
IDE integration (ACP) No ACP bridge for Codex, Claude Code

A generic MCP client gives you the protocol. OpenClaw gives you the orchestration layer built on top of it.

How OpenClaw Compares to Google ADK and Dynatrace

Google ADK

Google's Agent Development Kit has solid MCP support and integrates cleanly with the Google Cloud ecosystem — Vertex AI, Cloud Run, BigQuery. If you're building agents that live entirely within GCP, ADK is a reasonable choice.

The limitation is vendor lock-in. ADK assumes Google Cloud infrastructure. You can't run it locally on a laptop with your own gateway daemon, route messages through Telegram, or manage a mixed fleet of agents across channels without significant custom work. OpenClaw runs wherever Node.js runs — local, VPS, or cloud — with no infrastructure dependency.

Dynatrace

Dynatrace's MCP integration is primarily observability-oriented. Their angle is using MCP to let AI agents query monitoring data, trigger workflows, and surface insights from their platform. It's genuinely useful if you're already deep in the Dynatrace ecosystem and want AI-driven ops.

But Dynatrace is not an orchestration framework. It doesn't manage agent sessions, route tools across agents, or provide a programmable multi-agent backbone. OpenClaw approaches the same MCP standard from the orchestration angle — the question isn't "how do I query my observability platform via MCP?" but "how do I wire 500 different tools into a production multi-agent system and control which agent can call what?"

Different problems. OpenClaw solves the orchestration one.

Building Your Own MCP Server for OpenClaw

The MCP ecosystem grows through community-built servers. If you have a service or capability you want to expose to OpenClaw agents, here's the structure:

my-mcp-server/
├── package.json
├── src/
│   ├── index.ts          # MCP server entry point
│   ├── tools/            # Tool definitions
│   │   ├── create.ts
│   │   ├── read.ts
│   │   └── update.ts
│   └── resources/        # Resource definitions (optional)
└── README.md
Enter fullscreen mode Exit fullscreen mode

Design principles for OpenClaw-compatible MCP servers:

  • Action-oriented names: create_issue, send_message, fetch_record — not vague like process or handle
  • Single purpose per tool: one tool does one thing; composition happens at the agent layer
  • Complete schemas: every input and output field described; agents use these to reason about tool usage
  • Structured output: return JSON objects, not unstructured strings; agents parse structured responses reliably
  • Env-based config: secrets via environment variables, never hardcoded; document required vars in README

Publish via npm. Once published, your server is discoverable through ClawHub (available as of v2026.3.22), OpenClaw's community registry for plugins and MCP servers.

# Install your published MCP server as an OpenClaw plugin
openclaw plugins install my-mcp-server
Enter fullscreen mode Exit fullscreen mode

Getting Started

If you want OpenClaw MCP orchestration running today:

1. Install OpenClaw

npm install -g openclaw
openclaw init
Enter fullscreen mode Exit fullscreen mode

2. Enable the Composio plugin for 500+ app integrations out of the box:

openclaw plugins install @composio/openclaw-plugin
Enter fullscreen mode Exit fullscreen mode

Then add your COMPOSIO_API_KEY to your environment and configure the plugin entry in openclaw.json.

3. Enable mcporter for direct CLI MCP access — it's a built-in skill, enabled by default. Run mcporter list after connecting a server to see available tools.

4. Build your own MCP server — follow the structure above, publish to npm, and list it on ClawHub.

Resources:

The multi-agent MCP future isn't locked to any cloud vendor. It runs wherever your agents run.

Top comments (0)