code-mode 0.0.1

Code execution mode for UTCP - enables executing Python code chains with tool access.

🐍 Python Code-Mode: First Python library for tool calls via code execution

Transform your AI agents from clunky tool callers into efficient code executors — in just 3 lines of Python.

Why This Changes Everything

LLMs excel at writing code but struggle with tool calls. Instead of exposing hundreds of tools directly, give them ONE tool that executes Python code with access to your entire toolkit.

Research from Apple, Cloudflare and Anthropic proves:

• 60% faster execution than traditional tool calling
• 68% fewer tokens consumed
• 88% fewer API round trips
• 98.7% reduction in context overhead for complex workflows

Benchmarks

Independent Python benchmark study validates the performance claims with $9,536/year cost savings at 1,000 scenarios/day:

Scenario Complexity	Traditional	Code Mode	Improvement
Simple (2-3 tools)	3 iterations	1 execution	67% faster
Medium (4-7 tools)	8 iterations	1 execution	75% faster
Complex (8+ tools)	16 iterations	1 execution	88% faster

Why Code Mode Dominates:

Batching Advantage - Single code block replaces multiple API calls
Cognitive Efficiency - LLMs excel at code generation vs. tool orchestration
Computational Efficiency - No context re-processing between operations

Real-world results: Independent benchmarks demonstrate significant cost savings, with $9,536/year savings possible at enterprise scale (1,000 scenarios/day).

Get Started in 3 Lines

from utcp_code_mode import CodeModeUtcpClient

client = await CodeModeUtcpClient.create()                      # 1. Initialize
await client.register_manual({'name': 'github', ...})           # 2. Add tools  
result = await client.call_tool_chain('# Python code here')     # 3. Execute code

That's it. Your AI agent can now execute complex workflows in a single request instead of dozens.

What You Get

Progressive Tool Discovery

# Agent discovers tools dynamically, loads only what it needs
tools = await client.search_tools('github pull request')
# Instead of 500 tool definitions → 3 relevant tools

Natural Code Execution

result = await client.call_tool_chain('''
# Chain multiple operations in one request
pr = await github.get_pull_request(owner='microsoft', repo='vscode', pull_number=1234)
comments = await github.get_pull_request_comments(owner='microsoft', repo='vscode', pull_number=1234)
reviews = await github.get_pull_request_reviews(owner='microsoft', repo='vscode', pull_number=1234)

# Process data efficiently in-sandbox
summary = {
    "title": pr["title"],
    "comment_count": len(comments),
    "approvals": len([r for r in reviews if r["state"] == "APPROVED"])
}

print(f'PR "{pr["title"]}" analysis complete')
return summary
''')

print('Analysis Result:', result["result"])
# console output: 'PR "Fix memory leak in hooks" analysis complete'

Auto-Generated Python TypedDict Interfaces

class GetPullRequestInput(TypedDict):
    """Repository owner"""
    owner: str
    """Repository name""" 
    repo: str
    """Pull request number"""
    pull_number: int

Enterprise-Ready

• Secure Subprocess Isolation – True process isolation prevents unauthorized access
• Timeout Protection – Configurable execution limits prevent runaway code
• Complete Observability – Full console output capture and error handling
• Zero External Dependencies – Tools only accessible through registered UTCP/MCP servers
• Runtime Introspection – Dynamic interface discovery for adaptive workflows

Universal Protocol Support

Works with any tool ecosystem:

Protocol	Description	Usage
MCP	Model Context Protocol servers	call_template_type: 'mcp'
HTTP	REST APIs with auto-discovery	call_template_type: 'http'
File	Local JSON/YAML configurations	call_template_type: 'file'
CLI	Command-line tool execution	call_template_type: 'cli'

Installation

pip install utcp-code-mode

Even Easier: Ready-to-Use MCP Server

Want Code Mode without any setup? Use our plug-and-play MCP server with Claude Desktop or any MCP client:

{
  "mcpServers": {
    "code-mode": {
      "command": "uvx",
      "args": ["utcp-code-mode-mcp"],
      "env": {
        "UTCP_CONFIG_FILE": "/path/to/your/.utcp_config.json"
      }
    }
  }
}

That's it! No installation, no Python knowledge required. The Code Mode MCP Server automatically:

• Downloads and runs the latest version via uvx
• Loads your tool configurations from JSON
• Provides code execution capabilities to Claude Desktop
• Gives you call_tool_chain as an MCP tool for Python execution

Perfect for non-developers who want Code Mode power in Claude Desktop!

Direct Python Usage

1. MCP Server Integration

Connect to any Model Context Protocol server:

from utcp_code_mode import CodeModeUtcpClient

client = await CodeModeUtcpClient.create()

# Connect to GitHub MCP server
await client.register_manual({
    'name': 'github',
    'call_template_type': 'mcp',
    'config': {
        'mcpServers': {
            'github': {
                'command': 'docker',
                'args': ['run', '-i', '--rm', '-e', 'GITHUB_PERSONAL_ACCESS_TOKEN', 'mcp/github'],
                'env': {'GITHUB_PERSONAL_ACCESS_TOKEN': os.environ.get('GITHUB_TOKEN')}
            }
        }
    }
})

2. Execute Multi-Step Workflows

Replace 15+ tool calls with a single code execution:

result = await client.call_tool_chain('''
# Traditional: 4 separate API round trips → Code Mode: 1 execution
pr = await github.get_pull_request(owner='microsoft', repo='vscode', pull_number=1234)
comments = await github.get_pull_request_comments(owner='microsoft', repo='vscode', pull_number=1234)
reviews = await github.get_pull_request_reviews(owner='microsoft', repo='vscode', pull_number=1234)
files = await github.get_pull_request_files(owner='microsoft', repo='vscode', pull_number=1234)

# Process data in-sandbox (no token overhead)
summary = {
    "title": pr["title"],
    "state": pr["state"],
    "author": pr["user"]["login"],
    "stats": {
        "comments": len(comments),
        "reviews": len(reviews), 
        "files_changed": len(files),
        "approvals": len([r for r in reviews if r["state"] == "APPROVED"])
    },
    "top_discussion": [
        {
            "author": c["user"]["login"],
            "preview": c["body"][:100] + "..."
        }
        for c in comments[:3]
    ]
}

print(f'PR "{pr["title"]}" analysis complete')
return summary
''')

print('Analysis Result:', result["result"])
# console output: 'PR "Fix memory leak in hooks" analysis complete'

---

Advanced Features

Multi-Protocol Tool Chains

Mix and match different tool ecosystems in a single execution:

# Register multiple tool sources
await client.register_manual({'name': 'github', 'call_template_type': 'mcp', ...})
await client.register_manual({'name': 'slack', 'call_template_type': 'http', ...})
await client.register_manual({'name': 'db', 'call_template_type': 'file', 'file_path': './db-tools.json'})  # Loads UTCP manual from JSON

result = await client.call_tool_chain('''
# Fetch PR data from GitHub (MCP)
pr = await github.get_pull_request(owner='company', repo='api', pull_number=42)

# Query deployment status from database (File)
deployment = await db.get_deployment_status(pr_id=pr["id"])

# Send notification to Slack (HTTP)
await slack.post_message(
    channel='#releases',
    text=f'PR #42 "{pr["title"]}" deployed to {deployment["environment"]}'
)

return {"pr": pr["title"], "environment": deployment["environment"]}
''')

Runtime Interface Introspection

Tools can dynamically discover and adapt to available interfaces:

result = await client.call_tool_chain('''
# Discover available tools at runtime
print('Available interfaces:', __interfaces)

# Get specific tool interface for validation
pr_interface = __get_tool_interface('github.get_pull_request')
print('PR tool expects:', pr_interface)

# Use interface info for dynamic workflows
has_slack_tools = 'namespace slack' in __interfaces
if has_slack_tools:
    await slack.post_message(channel='#dev', text='Analysis complete')

return {"tools_available": has_slack_tools}
''')

Context-Efficient Data Processing

Process large datasets without bloating the model's context:

result = await client.call_tool_chain('''
# Fetch large dataset
all_issues = await github.list_repository_issues(owner='facebook', repo='react')
print(f'Fetched {len(all_issues)} total issues')

# Process efficiently in-sandbox
critical_bugs = [
    {
        "number": issue["number"],
        "title": issue["title"],
        "author": issue["user"]["login"],
        "days_old": (datetime.now() - datetime.fromisoformat(issue["created_at"].replace('Z', '+00:00'))).days
    }
    for issue in all_issues
    if any(l["name"] == "bug" for l in issue.get("labels", []))
    and any(l["name"] == "high priority" for l in issue.get("labels", []))
]
critical_bugs.sort(key=lambda x: x["days_old"], reverse=True)

# Only return processed summary (not 10,000 raw issues)
return {
    "total_issues": len(all_issues),
    "critical_bugs": critical_bugs[:10],  # Top 10 oldest critical bugs
    "summary": f'Found {len(critical_bugs)} critical bugs, oldest is {critical_bugs[0]["days_old"]} days old'
}
''')

Error Handling & Observability

Built-in error handling with complete execution transparency:

result = await client.call_tool_chain('''
try:
    print('Starting multi-step workflow...')
    
    data = await external_api.fetch_data(id='user-123')
    print('Data fetched successfully')
    
    processed = await data_processor.transform(data)
    print(f'Processing completed with {len(processed.get("warnings", []))} warnings')
    
    return processed
except Exception as error:
    print(f'Workflow failed: {error}')
    raise error  # Propagates to outer error handling
''', timeout=30)  # 30-second timeout

# Complete observability
print('Result:', result["result"])
print('Execution logs:', result["logs"])
# ['Starting multi-step workflow...', 'Data fetched successfully', 'Processing completed with 2 warnings']

Custom Timeouts

Configure execution limits for different workload types:

# Quick operations (5 seconds)
quick_result = await client.call_tool_chain('return await ping.check()', timeout=5)

# Heavy data processing (2 minutes) 
heavy_result = await client.call_tool_chain('''
big_data = await database.export_full_dataset()
return await analytics.process_dataset(big_data)
''', timeout=120)

---

AI Agent Integration

Plug-and-play with any AI framework. The built-in prompt template handles all the complexity:

from utcp_code_mode import CodeModeUtcpClient
from openai import OpenAI

system_prompt = f"""
You are an AI assistant with access to tools via UTCP CodeMode.
{CodeModeUtcpClient.AGENT_PROMPT_TEMPLATE}
Additional instructions...
"""

# Works with any AI library
client = OpenAI()
response = client.chat.completions.create(
    model='gpt-4',
    messages=[
        {'role': 'system', 'content': system_prompt},
        {'role': 'user', 'content': 'Analyze the latest PR in microsoft/vscode'}
    ]
)

The template provides comprehensive guidance on:

• Tool discovery workflow (search_tools → __interfaces → call_tool_chain)
• Hierarchical access patterns (manual.tool() syntax)
• Interface introspection (__get_tool_interface())
• Error handling and best practices

---

API Reference

Core Methods

call_tool_chain(code: str, timeout: int = 30)

Execute Python code with full tool access and observability.

• Returns: {"result": Any, "logs": List[str]} with execution result and captured console output
• Default timeout: 30 seconds

get_all_tools_python_interfaces()

Generate complete Python TypedDict interfaces for IDE integration.

• Returns: String containing all interface definitions with proper typing

search_tools(query: str) (from UtcpClient)

Discover tools using natural language queries.

• Returns: List of relevant tools with descriptions and interfaces

Static Methods

CodeModeUtcpClient.create(root_dir=None, config=None)

Create a new client instance with optional configuration.

CodeModeUtcpClient.AGENT_PROMPT_TEMPLATE

Production-ready prompt template for AI agents.

---

Security & Performance

Secure by Design

• Subprocess isolation – True process isolation with separate memory space
• No filesystem access – Tools only through registered servers
• Timeout protection – Configurable execution limits with force termination
• Restricted imports – Only safe modules allowed (json, math, asyncio, datetime, etc.)
• Safe builtins – Dangerous functions like exec, eval, open blocked

Performance Optimized

• Minimal memory footprint – Subprocess cleanup after execution
• Efficient tool caching – Python interfaces cached automatically
• Streaming console output – Real-time log capture without buffering
• Identifier sanitization – Handles invalid Python identifiers gracefully

---

Security Model

The CodeModeUtcpClient implements a cooperative sandbox designed for LLM-generated code execution:

Security Features

• Subprocess Isolation: Code executes in separate processes for true timeout and memory isolation
• Restricted Imports: Only safe modules (json, math, asyncio, datetime, time, re, etc.) can be imported
• Safe Builtins: Limited set of built-in functions, dangerous ones like exec, eval, open are blocked
• No System Access: Modules like os, sys, subprocess are not available in execution context
• Real Timeouts: Processes can be forcibly terminated if they exceed time limits
• Error Isolation: Exceptions and errors are properly contained and reported

Allowed Modules

• json - JSON parsing and serialization
• math - Mathematical functions
• asyncio - Async/await support (required for tool calls)
• datetime - Date and time utilities
• time - Time functions
• re - Regular expressions
• typing - Type hints
• collections, itertools, functools, operator, uuid - Standard utilities

Blocked Features

• System modules: os, sys, subprocess, shutil
• File I/O: open, file operations outside tools
• Network: socket, urllib, requests
• Code execution: exec, eval, compile
• Introspection abuse: Direct __builtins__ manipulation

Use Case

This security model is designed for:

• Cooperative LLM-generated code (not adversarial)
• Tool-based workflows where tools provide controlled external access
• Agent task execution with defined interfaces
• Educational and development environments
• Internal automation with trusted code sources

Not suitable for: Production multi-tenant environments running untrusted user code.

---

Development Experience

IDE Integration

Generate Python type definitions for full IntelliSense support:

# Generate tool interfaces  
interfaces = await client.get_all_tools_python_interfaces()
with open('generated_tools.py', 'w') as f:
    f.write(interfaces)

# Import in your code for type hints
from generated_tools import GithubNamespace

Debug & Monitor

Built-in observability for production deployments:

result = await client.call_tool_chain(user_code)

# Ship logs to your monitoring system
for log in result["logs"]:
    if log.startswith('[ERROR]'):
        monitoring.error(log)
    elif log.startswith('[WARN]'):
        monitoring.warn(log)

---

Why Choose Code Mode UTCP?

Traditional Tool Calling	Code Mode UTCP	Improvement
15+ API round trips	1 code execution	15x fewer requests
50,000+ context tokens	2,000 tokens	96% token reduction
16 iterations (complex)	1 iteration	88% faster
Higher token costs	68% token reduction	$9,536/year savings
Manual error handling	Automatic capture & logs	Zero-config observability
Tool-by-tool discovery	Dynamic semantic search	Progressive disclosure
Vendor/protocol lock-in	Universal compatibility	MCP, HTTP, File, CLI

Benchmark Methodology

The comprehensive Python study tested 16 realistic scenarios across:

• Financial workflows (invoicing, expense tracking)
• DevOps operations (deployments, monitoring)
• Data processing (analysis, reporting)
• Business automation (CRM, notifications)

Models tested: Claude Haiku, Gemini Flash
Pricing basis: $0.25/1M input, $1.25/1M output tokens
Scale: 1,000 scenarios/day = $9,536/year savings with Code Mode

Learn More

• Cloudflare Research – Original code mode whitepaper
• Anthropic Study – MCP code execution benefits
• Python Benchmark Study – Comprehensive performance analysis
• UTCP Specification – Official Python implementation
• Report Issues – Bug reports and feature requests

License

MPL-2.0 – Open source with commercial-friendly terms.