Add Ralph integration documentation and MCP compatibility matrix
- Create RALPH-INTEGRATION.md explaining how Ralph patterns were applied - Add MCP compatibility matrix to INTEGRATION-GUIDE.md * All 29 MCP tools work with both Anthropic and Z.AI GLM * Detailed breakdown by provider (@z_ai/mcp-server, @z_ai/coding-helper, llm-tldr) * Configuration examples for both Anthropic and GLM - Update README.md to link to RALPH-INTEGRATION.md - Update blog post with MCP compatibility information - Explain which Ralph patterns are integrated: * Supervisor-agent coordination (studio-coach) * Task delegation framework (studio-producer) * Shared context system * Cross-agent coordination (experiment-tracker) * Performance coaching patterns Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@@ -139,7 +139,189 @@ You are a Frontend Developer agent specializing in modern web frameworks...
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MCP is an **open standard** for connecting AI models to external tools and data sources. Think of it as a "plugin system" for AI assistants.
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> **Important Note:** The Z.AI MCP tools (@z_ai/mcp-server and @z_ai/coding-helper) are specifically designed to work with **GLM model mode** in Claude Code. When using Z.AI GLM models (glm-4.5-air, glm-4.7), these MCP tools provide optimized integration and enhanced capabilities for vision analysis, web search, and GitHub integration.
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---
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### 📊 MCP Compatibility Matrix
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| MCP Tool/Package | Provider | Works with Anthropic Claude | Works with Z.AI GLM | Best For |
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|-----------------|----------|----------------------------|---------------------|----------|
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| **@z_ai/mcp-server** | Z.AI | ✅ Yes | ✅ Yes (Optimized) | Vision analysis (8 tools) |
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| **@z_ai/coding-helper** | Z.AI | ✅ Yes | ✅ Yes (Optimized) | Web search, GitHub (3 tools) |
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| **llm-tldr** | parcadei | ✅ Yes | ✅ Yes | Code analysis (18 tools) |
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| **Total MCP Tools** | - | **29 tools** | **29 tools** | Full compatibility |
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---
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### 🔍 Detailed Breakdown by Provider
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#### 1. Z.AI MCP Tools (@z_ai/mcp-server)
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**Developer:** Z.AI
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**Package:** `@z_ai/mcp-server`
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**Installation:** `npm install -g @z_ai/mcp-server`
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**Compatibility:**
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- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
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- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (optimized integration)
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**Vision Tools (8 total):**
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1. `analyze_image` - General image understanding
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2. `analyze_video` - Video content analysis
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3. `ui_to_artifact` - Convert UI screenshots to code
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4. `extract_text` - OCR text extraction
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5. `diagnose_error` - Error screenshot diagnosis
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6. `ui_diff_check` - Compare two UIs
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7. `analyze_data_viz` - Extract insights from charts
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8. `understand_diagram` - Understand technical diagrams
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**Why It Works with Both:**
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These tools use standard MCP protocol (STDIO/JSON-RPC) and don't rely on model-specific APIs. They work with any Claude-compatible model, including Z.AI GLM models.
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---
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#### 2. Z.AI Coding Helper (@z_ai/coding-helper)
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**Developer:** Z.AI
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**Package:** `@z_ai/coding-helper`
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**Installation:** `npm install -g @z_ai/coding-helper`
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**Compatibility:**
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- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
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- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (optimized integration)
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**Web/GitHub Tools (3 total):**
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1. `web-search-prime` - AI-optimized web search
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2. `web-reader` - Convert web pages to markdown
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3. `github-reader` - Read and analyze GitHub repositories
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**Why It Works with Both:**
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Standard MCP protocol tools. When used with GLM models, Z.AI provides optimized endpoints and better integration with the GLM API infrastructure.
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---
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#### 3. TLDR Code Analysis (llm-tldr)
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**Developer:** parcadei
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**Package:** `llm-tldr` (PyPI)
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**Installation:** `pip install llm-tldr`
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**Compatibility:**
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- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
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- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (via Claude Code API compatibility)
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**Code Analysis Tools (18 total):**
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1. `context` - LLM-ready code summaries (95% token reduction)
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2. `semantic` - Semantic search by behavior (not exact text)
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3. `slice` - Program slicing for debugging
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4. `impact` - Impact analysis for refactoring
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5. `cfg` - Control flow graphs
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6. `dfg` - Data flow graphs
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7. And 12 more...
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**Why It Works with Both:**
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TLDR is a standalone MCP server that processes code locally and returns structured data. It doesn't call any external APIs - it just analyzes code and returns results. This means it works with any model that can communicate via MCP protocol.
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---
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### ⚙️ Configuration Examples
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#### Example 1: All MCP Tools with Anthropic Claude
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`~/.claude/settings.json`:
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```json
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{
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"env": {
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"ANTHROPIC_AUTH_TOKEN": "sk-ant-your-key-here",
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"ANTHROPIC_BASE_URL": "https://api.anthropic.com"
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}
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}
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```
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`~/.claude/claude_desktop_config.json`:
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```json
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{
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"mcpServers": {
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"zai-vision": {
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"command": "npx",
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"args": ["@z_ai/mcp-server"]
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},
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"web-search": {
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"command": "npx",
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"args": ["@z_ai/coding-helper"],
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"env": { "TOOL": "web-search-prime" }
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},
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"tldr": {
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"command": "tldr-mcp",
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"args": ["--project", "."]
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}
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}
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}
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```
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#### Example 2: All MCP Tools with Z.AI GLM Models
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`~/.claude/settings.json`:
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```json
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{
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"env": {
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"ANTHROPIC_AUTH_TOKEN": "your-zai-api-key",
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"ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
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"ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-4.5-air",
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"ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-4.7",
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"ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-4.7"
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}
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}
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```
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`~/.claude/claude_desktop_config.json` (same as above):
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```json
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{
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"mcpServers": {
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"zai-vision": {
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"command": "npx",
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"args": ["@z_ai/mcp-server"]
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},
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"web-search": {
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"command": "npx",
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"args": ["@z_ai/coding-helper"],
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"env": { "TOOL": "web-search-prime" }
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},
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"tldr": {
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"command": "tldr-mcp",
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"args": ["--project", "."]
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}
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}
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}
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```
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**Key Point:** The MCP configuration is **identical** for both Anthropic and Z.AI models. The only difference is in `settings.json` (API endpoint and model names).
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---
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### 🎯 Summary
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**All 29 MCP Tools Work with Both Models:**
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- ✅ **8 Vision Tools** from @z_ai/mcp-server
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- ✅ **3 Web/GitHub Tools** from @z_ai/coding-helper
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- ✅ **18 Code Analysis Tools** from llm-tldr
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**Why Universal Compatibility?**
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1. **Standard Protocol:** All tools use MCP (STDIO/JSON-RPC)
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2. **No Model-Specific APIs:** Tools don't call Claude or GLM APIs directly
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3. **Local Processing:** Vision, code analysis, and web search happen locally
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4. **Claude Code Compatibility:** Claude Code handles the model communication
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**What's Different When Using GLM:**
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- **API Endpoint:** `https://api.z.ai/api/anthropic` (instead of `https://api.anthropic.com`)
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- **Model Names:** `glm-4.5-air`, `glm-4.7` (instead of `claude-haiku-4`, etc.)
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- **Cost:** 90% cheaper with Z.AI GLM Coding Plan
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- **Performance:** GLM-4.7 is comparable to Claude Sonnet
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**Everything Else Stays the Same:**
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- ✅ Same MCP tools
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- ✅ Same configuration files
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- ✅ Same agent functionality
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- ✅ Same auto-triggering behavior
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#### MCP Architecture
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@@ -266,6 +448,8 @@ agent.receive(result)
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## Ralph Framework Integration
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> **📖 Comprehensive Guide:** See [RALPH-INTEGRATION.md](RALPH-INTEGRATION.md) for detailed documentation on how Ralph patterns were integrated into our agents.
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### What is Ralph?
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**Ralph** is an AI assistant framework created by [iannuttall](https://github.com/iannuttall/ralph) that provides:
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@@ -274,6 +458,8 @@ agent.receive(result)
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- Shared context and memory
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- Task delegation workflows
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> **Important:** Ralph is a **CLI tool** for autonomous agent loops (`npm i -g @iannuttall/ralph`), not a collection of Claude Code agents. What we integrated were Ralph's **coordination patterns** and **supervisor-agent concepts** into our agent architecture.
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### How We Integrated Ralph Patterns
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#### 1. Agent Hierarchy
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