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GLM 4.6 MCP server with custom json config for integration with Warp Terminal
Configuration
View docs{
"mcpServers": {
"bobvasic-glm-mcp-server": {
"command": "node",
"args": [
"/absolute/path/to/glm-mcp-server/build/index.js"
],
"env": {
"GLM_API_KEY": "your_glm_api_key_here"
}
}
}
}You set up a bidirectional MCP bridge that lets Claude 4.5 Sonnet access GLM-4.6βs architectural expertise in real time. This server exposes GLM-4.6βs capabilities through standard MCP tools, enabling seamless integration with Warp Terminalβs agent infrastructure to assist you with system design, scalability, security, and code analysis.
Interact with the GLM-4.6 MCP server through the MCP client inside Warp Terminal. You can request architectural guidance, analyze code for architectural quality, design complete system architectures, and review technical decisions. Each tool returns targeted advice, trade-offs, and actionable recommendations that fit your current technology stack and constraints.
Prerequisites include a supported runtime and toolchain: Node.js version 18.0.0 or newer and npm version 9.0.0 or newer. You also need a GLM-4.6 API key from the GLM provider.
# Prerequisites
node --version # must be >= 18.0.0
npm --version # must be >= 9.0.0
# Obtain API key from GLM provider and store as GLM_API_KEYClone the MCP server repository, install dependencies, and build the project.
cd glm-mcp-server
npm install
npm run buildCreate a local environment file to store your API key securely. Place the key in a file named .env at the project root.
GLM_API_KEY=your_api_key_hereConfigure security considerations and ensure .env is not committed to version control.
Warp Terminal integration requires a local MCP server process running and reachable by the Warp client. The recommended local development flow starts the MCP server via a Node.js process that loads the built index file.
To verify the server starts correctly, you can run the server directly in development mode and observe the startup message.
General architectural consultation for system design patterns, scalability strategies, and technical guidance.
Architectural analysis of source code including design patterns, SOLID principles, and improvement recommendations.
Complete system architecture design from requirements including component breakdown, data flow, and deployment strategies.
Technical decision review with impact assessment, trade-off analysis, and alternative recommendations.