A comprehensive utility skill for installing, validating, and orchestrating official GLM MCP servers within the OpenClaw ecosystem.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install glm-mcp-server-use
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 glm-mcp-server-use 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 GLM MCP Server Use?
GLM MCP Server Use is a specialized toolkit designed to streamline the integration of official Z.AI and GLM Model Context Protocol (MCP) servers. This skill enables developers to connect their AI agents to powerful external capabilities including real-time web search, document reading, repository analysis, and computer vision. By leveraging Openclaw Skills architecture, it provides a standardized way to handle endpoint wiring and authentication.
The skill acts as a bridge between OpenClaw and the four primary GLM servers: Vision, Web Search, Web Reader, and Zread. It simplifies the complex process of managing remote HTTP and local stdio connections, ensuring that your AI agent has reliable access to the latest web data and visual processing tools while maintaining secure API key management.
GLM MCP Server Use Use Cases
- Automating the installation of the official Z.AI MCP suite for new AI agent environments.
- Validating MCP server connectivity and tool schemas through automated smoke tests.
- Enhancing AI agents with real-time web browsing and search capabilities using Openclaw Skills.
- Troubleshooting parameter mismatches between agent calls and live MCP server schemas.
- Securely managing Z.AI API keys across different development environments using masked resolution scripts.
How GLM MCP Server Use Works
- API Resolution: The skill scans environment variables (Z_AI_API_KEY, GLM_API_KEY, etc.) to securely resolve credentials without hardcoding.
- Configuration Generation: A dedicated setup script generates a standardized mcporter-glm.json configuration file to map all four GLM services.
- Schema Inspection: The skill uses mcporter to query the live MCP servers, ensuring the agent uses the exact tool names and parameter requirements currently exposed by the API.
- Validation: A smoke test script is executed to perform trial calls to each server, verifying that the vision, search, and reader components are responsive.
- Execution: Once validated, the Openclaw Skills workflow allows for direct tool calls like web_search_prime or analyze_image with structured JSON outputs.
GLM MCP Server Use Setup
To get started with GLM MCP Server Use in your project, follow these steps:
- Set your API key in the environment:
export Z_AI_API_KEY="your_zai_api_key"
- Run the setup script to generate your configuration:
python3 scripts/setup_glm_mcp_servers.py --config ./tmp/mcporter-glm.json
- Execute a smoke test to ensure everything is wired correctly:
python3 scripts/smoke_test_glm_mcp.py --config ./tmp/mcporter-glm.json
GLM MCP Server Use Data Schema & Taxonomy
The skill organizes its configuration and reporting data within a structured directory for clear auditability:
| File/Path | Description |
|---|---|
./tmp/mcporter-glm.json |
The primary configuration file containing server endpoints and auth headers. |
./tmp/glm-mcp-smoke-report.json |
Detailed output of the latest validation run including success/failure logs. |
scripts/get_zai_api_key.py |
Utility for masked API key verification. |
references/ |
Directory containing the server matrix, endpoint notes, and tested behavior records. |
GLM MCP Server Use Advanced Features
- Multi-Variable Key Resolution: Supports four different environment variable names to ensure compatibility with various CI/CD setups.
- Live Schema Introspection: Dynamically fetches current server capabilities to prevent breaking changes when remote tools update.
- Masked Token Validation: Check for key existence and validity without exposing the full sensitive token in logs or CLI output.
- Cross-Protocol Support: Manages both remote HTTP endpoints (Search/Reader/Zread) and local stdio instances (Vision) simultaneously.
- Detailed Smoke Reporting: Generates structured JSON reports for integration into automated testing pipelines.
SKILL.md
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