GLM-4.6 MCP Server
Enables Claude to consult GLM-4.6's architectural intelligence for system design, code analysis, scalability patterns, and technical decision-making. Provides specialized tools for enterprise architecture consultation, distributed systems design, and code review through the Model Context Protocol.
README
<div align="center">

<span style="color: #1e3a8a">GLM-4.6 MCP Server</span>
<span style="color: #10b981">Enterprise Architecture Consultation Protocol</span>
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.
</div>
<span style="color: #1e3a8a">🏗️ System Overview</span>
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.
<span style="color: #10b981">Architectural Capabilities</span>
- 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
<span style="color: #1e3a8a">⚡ Quick Start</span>
<span style="color: #10b981">Prerequisites</span>
node >= 18.0.0
npm >= 9.0.0
GLM-4.6 API Key from https://open.bigmodel.cn
<span style="color: #10b981">Installation</span>
cd glm-mcp-server
npm install
npm run build
<span style="color: #10b981">Environment Configuration</span>
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.
<span style="color: #1e3a8a">🔧 Warp Terminal Integration</span>
<span style="color: #10b981">MCP Server Configuration</span>
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 path - Replace
your_glm_api_key_herewith your actual GLM API key - Restart Warp Terminal after configuration changes
<span style="color: #10b981">Verification</span>
# Test server functionality
node build/index.js
# Expected output: "GLM-4.6 MCP Server running on stdio"
<span style="color: #1e3a8a">📡 MCP Tools Reference</span>
<span style="color: #10b981">1. consult_architecture</span>
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
<span style="color: #10b981">2. analyze_code_architecture</span>
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
<span style="color: #10b981">3. design_system_architecture</span>
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
<span style="color: #10b981">4. review_technical_decision</span>
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
<span style="color: #1e3a8a">🔬 Usage Examples</span>
<span style="color: #10b981">Example 1: Architectural Consultation</span>
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"
})
<span style="color: #10b981">Example 2: Code Architecture Analysis</span>
analyze_code_architecture({
code: `class UserService { ... }`,
language: "typescript",
question: "Does this service follow clean architecture principles?"
})
<span style="color: #10b981">Example 3: System Design</span>
design_system_architecture({
requirements: `
- Real-time messaging platform
- 1M concurrent users
- Sub-100ms latency
- 99.99% uptime SLA
- Global distribution
`
})
<span style="color: #1e3a8a">🏛️ Architecture</span>
┌─────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────┘
<span style="color: #1e3a8a">🛠️ Development</span>
<span style="color: #10b981">Build</span>
npm run build # Compile TypeScript to JavaScript
npm run watch # Development mode with auto-rebuild
<span style="color: #10b981">Project Structure</span>
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)
<span style="color: #1e3a8a">🔐 Security Considerations</span>
- 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
<span style="color: #1e3a8a">📊 Performance Characteristics</span>
| 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) |
<span style="color: #1e3a8a">🐛 Troubleshooting</span>
<span style="color: #10b981">Server Not Starting</span>
# 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
<span style="color: #10b981">API Authentication Errors</span>
- Verify API key validity at https://open.bigmodel.cn
- Check API key has sufficient quota
- Ensure no whitespace in
.envfile
<span style="color: #10b981">Warp Terminal Integration Issues</span>
- Restart Warp Terminal after configuration changes
- Verify absolute path in MCP configuration
- Check Warp logs: Warp → Settings → Advanced → View Logs
<span style="color: #1e3a8a">📚 Resources</span>
- 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
<span style="color: #1e3a8a">📝 License</span>
MIT License - Copyright (c) 2025 CyberLink Security
<span style="color: #1e3a8a">🤝 Support</span>
Enterprise Support: info@cyberlinksec.com
Issue Reporting: Include server logs, Warp version, and reproduction steps
<div align="center">
<span style="color: #10b981">Built with Enterprise Standards by CyberLink Security</span>
Empowering AI-Driven Architecture Decision Intelligence
</div>
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。