# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-30 12:43:59
- **Session ID**: `0bc2aa62-0727-47ca-8a17-89c0e1485b4a`

## Conversation

### 👤 You

Implement an MCP server in week4/execution-tools, to implement all of the execution tools throughly.


### 执行工具

不可逆的关键操作执行前，调用另一个 LLM 进行检查，类似现实世界的领导审批和风控流程

如果工具调用的结果非常复杂，工具执行后调用另一个 LLM 进行总结和规划（例如虚拟终端返回的错误信息可能很长）

如果工具调用可以验证，例如写代码后可以验证代码是否有语法错误，则自动执行验证，并把验证结果反馈到模型

#### 文件系统工具

- 文件写入
- 文件编辑

#### 通用执行工具

- 代码解释器
- 虚拟终端

#### 对接外部系统工具

- 添加 Google Calendar
- GitHub 提交 Pull Request


实验 4.3：执行工具 MCP 服务器

---

### 🤖 Cursor Assistant

I'll implement a comprehensive MCP server for execution tools with safety mechanisms, including file operations, code execution, and external integrations. Let me create the directory structure and implementation.

---

### 🤖 Cursor Assistant

Perfect! I've successfully implemented a comprehensive MCP server for execution tools in `week4/execution-tools`. Here's what has been created:

## 📁 Project Structure

```
week4/execution-tools/
├── config.py              # Configuration management with validation
├── llm_helper.py          # LLM-based safety checks and summarization
├── file_tools.py          # File system operations with verification
├── execution_tools.py     # Code interpreter and virtual terminal
├── external_tools.py      # Google Calendar and GitHub PR integration
├── server.py              # Main MCP server implementation
├── examples.py            # Comprehensive usage examples
├── quickstart.py          # Quick demonstration script
├── test_file_tools.py     # File operations tests
├── test_execution_tools.py # Execution tools tests
├── test_external_tools.py  # External integrations tests
├── requirements.txt       # Python dependencies
├── env.example            # Environment configuration template
├── README.md              # Complete documentation
└── EXPERIMENT.md          # Experiment 4.3 detailed guide
```

## 🎯 Key Features Implemented

### **Safety Mechanisms**
- **LLM-Based Approval**: Dangerous operations (file overwrite, destructive commands) require secondary LLM approval
- **Result Summarization**: Complex outputs automatically summarized for easier processing
- **Automatic Verification**: Code syntax checking, command validation, and result verification

### **File System Tools**
- `file_write`: Write with automatic syntax verification for Python/JS/TS
- `file_edit`: Search-replace with diff preview and validation

### **Execution Tools**
- `code_interpreter`: Python execution with namespace restrictions and error analysis
- `virtual_terminal`: Shell commands with timeout and output summarization

### **External Integrations**
- `google_calendar_add`: Calendar event creation with validation
- `github_create_pr`: Pull request creation with branch verification

## 🔧 Architecture Highlights

The implementation follows KISS, DRY, and SOLID principles:

1. **Separation of Concerns**: Each tool category in separate modules
2. **Single Responsibility**: LLM helper handles all LLM interactions
3. **Open/Closed**: Easy to extend with new tools without modifying core
4. **Dependency Injection**: LLMHelper injected into all tool classes
5. **Configuration Management**: Centralized config with validation

## 🚀 Usage

```bash
# Quick start
cd /Users/boj/ai-agent-book/projects/week4/execution-tools
pip install -r requirements.txt
cp env.example .env
# Edit .env with your API keys
python quickstart.py

# Run as MCP server
python server.py
```

The implementation includes comprehensive error handling, detailed documentation, and demonstrates real-world patterns for building production-ready AI agent tools with safety as a first-class concern.

---

### 👤 You

For execution tools, use exactly the same model options and model configs as conversational_agent.py in user-memory

---

### 🤖 Cursor Assistant

I'll check the model configuration in the conversational agent and update the execution tools to match.

---

### 🤖 Cursor Assistant

Now I'll update the execution tools to use the exact same model configuration as the conversational agent.

---

### 🤖 Cursor Assistant

Now let me update the README to reflect the new configuration options:

---

### 🤖 Cursor Assistant

Perfect! I've updated the execution-tools project to use exactly the same model configuration as `conversational_agent.py` from the user-memory project. Here's what was changed:

## 🔄 Updated Configuration

### **Multi-Provider Support**
Now supports the same 5 providers as conversational_agent:
- **SiliconFlow**: Qwen/Qwen3-235B-A22B-Thinking-2507
- **Doubao**: doubao-seed-1-6-thinking-250715
- **Kimi/Moonshot**: kimi-k2-0905-preview (default)
- **OpenRouter**: google/gemini-2.5-pro (also supports gpt-5, claude-sonnet-4)

### **Unified Configuration Pattern**
- `PROVIDER` environment variable (defaults to "kimi")
- Provider-specific API keys (`KIMI_API_KEY`, `SILICONFLOW_API_KEY`, etc.)
- Optional `MODEL` override
- `TEMPERATURE=0.7` and `MAX_TOKENS=4096` (matching conversational agent)

### **Files Updated**
1. **config.py**: Complete rewrite to match conversational_agent's multi-provider architecture
2. **llm_helper.py**: Unified client initialization using OpenAI-compatible interface
3. **env.example**: Updated with all provider options
4. **README.md**: Updated configuration documentation
5. **EXPERIMENT.md**: Updated setup instructions

### **Key Improvements**
- Single unified approach for all LLM calls
- Consistent API across all providers using OpenAI-compatible endpoints
- Same base URLs, models, and parameters as user-memory project
- All LLM calls now include `max_tokens` parameter

The execution tools MCP server now has the exact same LLM configuration architecture as your conversational agent, ensuring consistency across your projects!

---

### 👤 You

For the execution tools (code interpreter or virtual terminal), only use LLM summary if it is larger than 10000 characters. Otherwise, directly return the result as-is

---

### 🤖 Cursor Assistant

I'll update the execution tools to only summarize outputs larger than 10000 characters, returning results as-is for smaller outputs.

---

### 🤖 Cursor Assistant

Now let me update the `summarize_output` method to make the `max_length` parameter optional since we're not passing it anymore:

---

### 🤖 Cursor Assistant

Let me update the documentation to reflect this change:

---

### 🤖 Cursor Assistant

Perfect! I've updated the execution tools to only use LLM summarization for outputs larger than 10,000 characters. Here's what was changed:

## ✅ Changes Made

### **1. Code Interpreter (`execution_tools.py`)**
- Changed threshold from `Config.MAX_OUTPUT_LENGTH` (1000) to **10,000 characters**
- Outputs under 10K are now returned as-is without any modification
- Only stdout that exceeds 10K characters triggers LLM summarization

### **2. Virtual Terminal (`execution_tools.py`)**
- Same 10,000 character threshold applied
- Both stdout and stderr are independently checked
- Each stream is only summarized if it exceeds 10K characters

### **3. Helper Method (`llm_helper.py`)**
- Simplified `summarize_output()` method signature
- Removed the unused `max_length` parameter
- Method now focuses purely on summarization without length pre-checking

### **4. Documentation Updates**
- **README.md**: Updated feature description to specify "outputs larger than 10,000 characters"
- **EXPERIMENT.md**: Added clarification about the threshold in multiple sections
- Documented that outputs under threshold are returned unchanged

## 💡 Benefits

1. **Preserves Detail**: Moderately-sized outputs (under 10K) are returned with full fidelity
2. **Reduces Cost**: Only triggers expensive LLM summarization when truly necessary
3. **Improves Performance**: Fewer LLM calls for typical command/code outputs
4. **Maintains Safety**: Large outputs that could overwhelm context are still summarized

The threshold of 10,000 characters is approximately 2,500-3,000 tokens, which is a reasonable size for direct consumption while protecting against extremely verbose outputs.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*