GLM 4.7 Server

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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?

Feature	Claude Opus	GLM-4.7
Cost per 1M tokens (input)	$15.00	~$2.00
SWE-Bench Verified	72.4%	73.8%
Terminal Bench 2.0	38.2	41.0
Savings	—	~87%

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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

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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 .

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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.

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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

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Tools

Tool	Description	Access	Best For
glm_ask	Quick questions	None	Explanations, brainstorming
glm_summarize	Summarize text	None	Docs, meeting notes
glm_explain	Explain code/concepts	None	Learning, understanding
glm_analyze	Analyze codebase	Read-only	Architecture, patterns
glm_review	Code review	Read-only	Quality, security, style
glm_find_bugs	Find potential bugs	Read-only	Debugging, QA
glm_implement	Implementation	Write	Features, refactoring
glm_refactor	Refactor code	Write	Code cleanup
glm_write_tests	Generate unit tests	Write	TDD, coverage
glm_document	Add documentation	Write	Docstrings, API docs
glm_generate_readme	Generate README.md	Write	Project docs
glm_status	Server status	—	Diagnostics
glm_compare_costs	Cost comparison	—	Budgeting

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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

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Model Selection

The server automatically maps Claude model names to GLM models:

Claude	GLM	Use Case
haiku	glm-4.5-air	Quick tasks, summaries
sonnet	glm-4.7	Balanced quality/speed
opus	glm-4.7	Highest quality

You can specify the model parameter in any tool:

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

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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

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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

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License

MIT License - see LICENSE for details.

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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

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Support

• Issues: GitHub Issues
• Z.ai Docs: docs.z.ai
• MCP Docs: modelcontextprotocol.io

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