GLM-4.7 MCP Server

Cost-efficient AI delegation for Claude Code

Features • Installation • Usage • Tools • Configuration

Overview

87% cost savings compared to Claude Opus while maintaining comparable quality for coding tasks.

The GLM-4.7 MCP Server is a Model Context Protocol server that routes tasks to Z.ai's GLM-4.7 model. It enables Claude Code to delegate work to a more cost-efficient AI model without sacrificing quality.

Why GLM-4.7?

Related MCP server: Zen MCP Server

Features

• 13 specialized tools for common development tasks

• Read-only and write-capable agents for safe delegation

• Automatic model selection (haiku for quick tasks, sonnet/opus for complex)

• Seamless Claude Code integration via MCP

• Cost tracking with built-in comparison tools

Installation

Prerequisites

1. Claude Code CLI - Install from claude.ai/download

   npm install -g @anthropic-ai/claude-code

2. Z.ai API Key - Get your key at z.ai/subscribe

   • GLM Coding Plan starts at ~1/7th the cost of Claude tiers

   • 3x the usage limits compared to Claude

Install the Server

# Clone the repository
git clone https://github.com/robertcprice/glm-mcp-server.git
cd glm-mcp-server

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e .

Configuration

1. Set Your API Key

Edit .env in the server directory:

ZAI_API_KEY=your_api_key_here

Or set as environment variable:

export ZAI_API_KEY=your_api_key_here

2. Add to Claude Desktop Config

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "glm": {
      "command": "/path/to/glm-mcp-server/.venv/bin/python",
      "args": ["/path/to/glm-mcp-server/server.py"],
      "env": {
        "ZAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

On Windows: %APPDATA%\Claude\claude_desktop_config.json On Linux: ~/.config/Claude/claude_desktop_config.json

3. Restart Claude Code

Restart Claude Code to load the new MCP server.

Usage

Once configured, the GLM tools are available in Claude Code:

Quick Questions

Use glm_ask to explain what this React hook does

Code Analysis

Use glm_analyze to review the authentication flow in src/auth/

Implementation

Use glm_implement to add user profile editing to the settings page

Cost Comparison

Use glm_compare_costs with 50000 input tokens and 20000 output tokens

Tools

Examples

Code Review

Use glm_review with review_focus="security" on src/api/auth.ts

Generate Tests

Use glm_write_tests for src/utils/validation.js with test_framework="jest"

Documentation

Use glm_document for src/services/user.py with style="google"

Bug Hunt

Use glm_find_bugs on src/components/payment/checkout.tsx

Model Selection

The server automatically maps Claude model names to GLM models:

You can specify the model parameter in any tool:

Use glm_ask with model="haiku" to quickly summarize this file

Development

Running the Server Directly

source .venv/bin/activate
python server.py

Running Tests

pip install pytest pytest-asyncio
pytest

Project Structure

glm-mcp-server/
├── server.py           # Main MCP server implementation
├── pyproject.toml      # Project configuration
├── .env                # API key (not in git)
├── .venv/              # Virtual environment
└── README.md           # This file

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

License

MIT License - see LICENSE for details.

Acknowledgments

• Anthropic for Claude Code and the MCP protocol

• Z.ai for the GLM-4.7 model and API

• FastMCP for the excellent MCP framework

Support

• Issues: GitHub Issues

• Z.ai Docs: docs.z.ai

• MCP Docs: modelcontextprotocol.io

Made with ❤️ for cost-effective AI development

⬆ Back to top