This MCP server bridges Claude 4.5 Sonnet with GLM-4.6's architectural intelligence for expert system design and technical consultation.
Core Capabilities:
• Architectural Consultation (consult_architecture) - Expert guidance on system design patterns, scalability strategies, distributed systems, microservices, event-driven architectures, and security patterns including threat modeling and zero-trust frameworks
• Code Architecture Analysis (analyze_code_architecture) - Evaluate source code for design patterns, SOLID principles, scalability concerns, security implications, and receive refactoring recommendations across multiple languages (TypeScript, Python, Go, Java, etc.)
• System Architecture Design (design_system_architecture) - Create complete system architectures from requirements with component breakdowns, data flow modeling, technology stack selection, and deployment strategies
• Technical Decision Review (review_technical_decision) - Assess technical decisions with impact analysis, trade-off evaluation, risk assessment, and alternative recommendations
Key Features: Horizontal scaling strategies, load balancing, caching hierarchies, service mesh architectures, authentication/authorization frameworks, and real-time consultation integrated into workflows via MCP protocol.
Integrates with Warp Terminal's agent infrastructure to provide real-time architectural consultation capabilities during development workflows
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@GLM-4.6 MCP Serveranalyze this microservice code for scalability issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.

GLM-4.6 MCP Server
Enterprise Architecture Consultation Protocol
Model Context Protocol bridge enabling Claude 4.5 Sonnet to leverage GLM-4.6's architectural intelligence for advanced system design, scalability patterns, and technical decision-making.
🏗️ System Overview
This MCP server establishes a bi-directional protocol bridge between Claude 4.5 Sonnet and GLM-4.6, enabling real-time architectural consultation during development workflows. The server exposes GLM-4.6's specialized capabilities through standardized MCP tools, facilitating seamless integration with Warp Terminal's agent infrastructure.
Architectural Capabilities
Distributed Systems Design: Microservices patterns, service mesh architectures, event-driven systems
Scalability Engineering: Horizontal scaling strategies, load balancing, caching hierarchies
Security Architecture: Threat modeling, zero-trust patterns, authentication/authorization frameworks
Code Analysis: SOLID principles evaluation, design pattern recognition, refactoring recommendations
Technical Decision Review: Trade-off analysis, risk assessment, alternative approach evaluation
System Architecture Design: Component decomposition, data flow modeling, technology stack selection
⚡ Quick Start
Prerequisites
node >= 18.0.0
npm >= 9.0.0
GLM-4.6 API Key from https://open.bigmodel.cnInstallation
cd glm-mcp-server
npm install
npm run buildEnvironment Configuration
Create .env file in project root:
GLM_API_KEY=your_api_key_hereSecurity Notice: Never commit .env to version control. Use secure secret management in production environments.
🔧 Warp Terminal Integration
MCP Server Configuration
Add the following configuration to your Warp MCP servers configuration file:
Location: ~/.config/warp-terminal/mcp_servers.json or Warp Settings → MCP Servers
{
"mcpServers": {
"glm-architecture": {
"command": "node",
"args": ["/absolute/path/to/glm-mcp-server/build/index.js"],
"env": {
"GLM_API_KEY": "your_glm_api_key_here"
}
}
}
}⚠️ Configuration Notes:
Replace
/absolute/path/to/glm-mcp-serverwith your actual installation pathReplace
your_glm_api_key_herewith your actual GLM API keyRestart Warp Terminal after configuration changes
Verification
# Test server functionality
node build/index.js
# Expected output: "GLM-4.6 MCP Server running on stdio"📡 MCP Tools Reference
1. consult_architecture
General architectural consultation for system design patterns, scalability strategies, and technical guidance.
Input Schema:
{
query: string; // Architectural question requiring expert consultation
context?: string; // Optional system context, requirements, constraints
}Use Case: High-level architectural decisions, pattern selection, scalability planning
2. analyze_code_architecture
Architectural analysis of source code including design patterns, SOLID principles, and improvement recommendations.
Input Schema:
{
code: string; // Source code to analyze
language: string; // Programming language (typescript, python, go, java, etc.)
question: string; // Specific architectural question about the code
}Use Case: Code review, refactoring planning, design pattern evaluation
3. design_system_architecture
Complete system architecture design from requirements including component breakdown, data flow, and deployment strategies.
Input Schema:
{
requirements: string; // Detailed system requirements, constraints, objectives
}Use Case: New system design, architecture documentation, technology selection
4. review_technical_decision
Technical decision review with impact assessment, trade-off analysis, and alternative recommendations.
Input Schema:
{
decision: string; // Technical decision to review
context: string; // Current architecture, constraints, objectives
}Use Case: Architecture review, technology evaluation, risk assessment
🔬 Usage Examples
Example 1: Architectural Consultation
Within Warp Terminal, Claude can invoke:
// Claude automatically calls via MCP
consult_architecture({
query: "What's the optimal caching strategy for a high-traffic API with 10k req/s?",
context: "Node.js microservices, PostgreSQL database, AWS infrastructure"
})Example 2: Code Architecture Analysis
analyze_code_architecture({
code: `class UserService { ... }`,
language: "typescript",
question: "Does this service follow clean architecture principles?"
})Example 3: System Design
design_system_architecture({
requirements: `
- Real-time messaging platform
- 1M concurrent users
- Sub-100ms latency
- 99.99% uptime SLA
- Global distribution
`
})🏛️ Architecture
┌─────────────────────────────────────────────────────────────┐
│ Warp Terminal │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Claude 4.5 Sonnet Agent │ │
│ └────────────────────┬─────────────────────────────────┘ │
└───────────────────────┼─────────────────────────────────────┘
│ MCP Protocol (stdio)
▼
┌─────────────────────────────────────────────────────────────┐
│ GLM MCP Server (Node.js) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ MCP Protocol Handler │ Tool Registry │ │
│ ├──────────────────────────────────────────────────────┤ │
│ │ GLM-4.6 API Client Layer │ │
│ │ • Authentication • Error Handling • Retry Logic │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬────────────────────────────────────┘
│ HTTPS/REST
▼
┌─────────────────────────────────────────────────────────────┐
│ GLM-4.6 API (open.bigmodel.cn) │
│ Zhipu AI Model Inference │
└─────────────────────────────────────────────────────────────┘🛠️ Development
Build
npm run build # Compile TypeScript to JavaScript
npm run watch # Development mode with auto-rebuildProject Structure
glm-mcp-server/
├── src/
│ ├── index.ts # MCP server entry point
│ └── glm-client.ts # GLM-4.6 API client
├── build/ # Compiled JavaScript output
├── package.json # Dependencies and scripts
├── tsconfig.json # TypeScript configuration
└── .env # Environment variables (not in VCS)🔐 Security Considerations
API Key Management: Store GLM_API_KEY in environment variables, never in code
Transport Security: All API communications use HTTPS/TLS
Input Validation: All tool inputs are validated before processing
Error Handling: Sensitive information is sanitized from error messages
Rate Limiting: Implement client-side rate limiting for production deployments
📊 Performance Characteristics
Metric | Specification |
Latency | 2-8s (model inference dependent) |
Throughput | API key tier dependent |
Timeout | 60s default (configurable) |
Max Token Output | 4096 tokens |
Concurrent Requests | Single instance: 1 (sequential processing) |
🐛 Troubleshooting
Server Not Starting
# Verify Node.js version
node --version # Must be >= 18.0.0
# Check build output
npm run build
# Verify GLM_API_KEY is set
echo $GLM_API_KEYAPI Authentication Errors
Verify API key validity at https://open.bigmodel.cn
Check API key has sufficient quota
Ensure no whitespace in
.envfile
Warp Terminal Integration Issues
Restart Warp Terminal after configuration changes
Verify absolute path in MCP configuration
Check Warp logs: Warp → Settings → Advanced → View Logs
📚 Resources
GLM-4.6 Documentation: https://docs.z.ai/guides/llm/glm-4.6
Model Context Protocol: https://modelcontextprotocol.io
Warp MCP Integration: https://docs.warp.dev/features/agent-mode/model-context-protocol
📝 License
MIT License - Copyright (c) 2025 CyberLink Security
🤝 Support
Enterprise Support: info@cyberlinksec.com
Issue Reporting: Include server logs, Warp version, and reproduction steps
Built with Enterprise Standards by CyberLink Security & Raptor Labs
Empowering AI-Driven Architecture Decision Intelligence