🏗️ 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.cn

Installation

cd glm-mcp-server
npm install
npm run build

Environment Configuration

Create .env file in project root:

GLM_API_KEY=your_api_key_here

Security 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-server with your actual installation path
• Replace your_glm_api_key_here with your actual GLM API key
• Restart 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-rebuild

Project 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_KEY

API Authentication Errors

• Verify API key validity at https://open.bigmodel.cn
• Check API key has sufficient quota
• Ensure no whitespace in .env file

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