A bridge for AI agents to interact with external tools and data sources via the Model Context Protocol.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mcp-adapter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mcp-adapter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MCP Integration Skill?
The MCP Integration skill is a foundational tool for modern AI development, allowing agents to move beyond static text generation by interacting with dynamic external environments. It implements the Model Context Protocol to provide a standardized way for agents to discover, understand, and call functions provided by remote servers. This skill is essential for developers using Openclaw Skills who want to build agents capable of real-time data retrieval and complex external operations.
By providing a unified interface for tool discovery and execution, this skill simplifies the process of integrating specialized services such as legal research databases, custom APIs, and database connectors. It ensures that agents can programmatically explore available capabilities and valid parameters through JSON Schema validation, making the interaction robust and reliable.
MCP Integration Skill Use Cases
- Accessing specialized legal or medical databases for up-to-date research and citation retrieval.
- Integrating real-time API data such as weather, financial markets, or transit updates into agent workflows.
- Connecting agents to internal corporate databases via secure MCP server connectors for data analysis.
- Building multi-stage automation workflows that chain different external tools to solve complex problems.
How MCP Integration Skill Works
- Discovery Phase: The agent triggers the list action to retrieve a comprehensive directory of all connected MCP servers and their available tools.
- Schema Inspection: The agent analyzes the inputSchema for a specific tool to identify required parameters, expected data types, and any constraints.
- Argument Validation: Before execution, the agent ensures all mandatory data is present and matches the technical requirements defined by the server.
- Execution Phase: The agent performs a call action, sending the server name, tool name, and arguments to the MCP integration.
- Result Parsing: The skill returns a structured response containing the output data or error details, which the agent then processes for the user.
MCP Integration Skill Setup
To integrate this capability into your agent environment, you must first configure your MCP server endpoints. Use the following command structure to verify your setup within the Openclaw Skills ecosystem:
# List all connected MCP tools to ensure communication is established
# This command verifies that your configured servers are reachable
mcp list
Ensure that your configuration file correctly maps server identifiers to their respective connection strings or executable paths.
MCP Integration Skill Data Schema & Taxonomy
The skill organizes its interactions using a structured metadata taxonomy to ensure compatibility across different servers:
| Property | Description |
|---|---|
| ID | A unique identifier in the format server:toolname. |
| inputSchema | A JSON Schema object defining valid parameters and required fields. |
| content | An array containing the result of the tool execution, usually as text or JSON string. |
| isError | A boolean flag indicating if the operation was successful or encountered an issue. |
All tool responses are returned in a standardized envelope, allowing for consistent error handling and data extraction across multiple Openclaw Skills instances.
MCP Integration Skill Advanced Features
- Tool Chaining: Enables complex workflows where the output of one MCP tool serves as the input for another.
- Dynamic Discovery: Automatically detects new tools and servers without requiring manual configuration restarts.
- Multi-Step Error Recovery: Provides detailed error responses that allow agents to self-correct and retry with modified arguments.
- Context Maintenance: Facilitates the extraction and preservation of relevant data points across sequential tool calls for long-running research tasks.
SKILL.md
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