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🔌 Plug-and-play library to enable agents to call MCP and UTCP tools via code execution.
Transform your AI agents from clunky tool callers into efficient code executors — in just 3 lines.
LLMs excel at writing code but struggle with tool calls. Instead of exposing hundreds of tools directly, give them ONE tool that executes TypeScript code with access to your entire toolkit.
Apple, Cloudflare, and Anthropic say that Code-Mode is a more efficient way to approach tool calling compared to the traditional dump function information and then extract a JSON for function calling.
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 |
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
import { CodeModeUtcpClient } from '@utcp/code-mode';
const client = await CodeModeUtcpClient.create(); // 1. Initialize
await client.registerManual({ name: 'github', /* MCP config */ }); // 2. Add tools
const { result } = await client.callToolChain(`/* TypeScript */`); // 3. Execute code
That's it. Your AI agent can now execute complex workflows in a single request instead of dozens.
// Agent discovers tools dynamically, loads only what it needs
const tools = await client.searchTools('github pull request');
// Instead of 500 tool definitions → 3 relevant tools
const { result, logs } = await client.callToolChain(`
// Chain multiple operations in one request
const pr = await github.get_pull_request({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
const comments = await github.get_pull_request_comments({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
const reviews = await github.get_pull_request_reviews({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
// Process data efficiently in-sandbox
return {
title: pr.title,
commentCount: comments.length,
approvals: reviews.filter(r => r.state === 'APPROVED').length
};
`);
// Single API call replaces 15+ traditional tool calls
namespace github {
interface get_pull_requestInput {
/** Repository owner */
owner: string;
/** Repository name */
repo: string;
/** Pull request number */
pull_number: number;
}
}
If you're working at an enterprise, and need support, book a consultation here.
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' |
npm install @utcp/code-mode
Want Code Mode without any setup? Use our plug-and-play MCP server with Claude Desktop or any MCP client:
{
"mcpServers": {
"code-mode": {
"command": "npx",
"args": ["@utcp/code-mode-mcp"],
"env": {
"UTCP_CONFIG_FILE": "/path/to/your/.utcp_config.json"
}
}
}
}
That's it! No installation, no Node.js knowledge required. The Code Mode MCP Server automatically:
npxcall_tool_chain as an MCP tool for TypeScript executionPerfect for non-developers who want Code Mode power in Claude Desktop!
Connect to any Model Context Protocol server:
Each
call_template_typeis a separate plugin package. Install and import the package once at startup so it can register itself with the client; the plugin registers as a side effect of being imported. Formcp, that's@utcp/mcp. The same pattern applies to other transports:@utcp/http,@utcp/text, etc.hljs language-bashnpm install @utcp/mcp
import '@utcp/mcp'; // registers the 'mcp' call template
import { CodeModeUtcpClient } from '@utcp/code-mode';
const client = await CodeModeUtcpClient.create();
// Connect to GitHub MCP server
await client.registerManual({
name: 'github',
call_template_type: 'mcp',
config: {
mcpServers: {
github: {
transport: 'stdio', // required by @utcp/mcp
command: 'docker',
args: ['run', '-i', '--rm', '-e', 'GITHUB_PERSONAL_ACCESS_TOKEN', 'mcp/github'],
env: { GITHUB_PERSONAL_ACCESS_TOKEN: process.env.GITHUB_TOKEN }
}
}
}
});
Replace 15+ tool calls with a single code execution:
const { result, logs } = await client.callToolChain(`
// Traditional: 4 separate API round trips → Code Mode: 1 execution
const pr = await github.get_pull_request({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
const comments = await github.get_pull_request_comments({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
const reviews = await github.get_pull_request_reviews({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
const files = await github.get_pull_request_files({ owner: 'microsoft', repo: 'vscode', pull_number: 1234 });
// Process data in-sandbox (no token overhead)
const summary = {
title: pr.title,
state: pr.state,
author: pr.user.login,
stats: {
comments: comments.length,
reviews: reviews.length,
filesChanged: files.length,
approvals: reviews.filter(r => r.state === 'APPROVED').length
},
topDiscussion: comments.slice(0, 3).map(c => ({
author: c.user.login,
preview: c.body.substring(0, 100) + '...'
}))
};
console.log(\`PR "\${pr.title}" analysis complete\`);
return summary;
`);
console.log('Analysis Result:', result);
// console output: 'PR "Fix memory leak in hooks" analysis complete'
Mix and match different tool ecosystems in a single execution:
// Register multiple tool sources
await client.registerManual({ name: 'github', call_template_type: 'mcp', /* config */ });
await client.registerManual({ name: 'slack', call_template_type: 'http', /* config */ });
await client.registerManual({ name: 'db', call_template_type: 'file', file_path: './db-tools.json' }); // This loads a UTCP manual from a json file
const result = await client.callToolChain(`
// Fetch PR data from GitHub (MCP)
const pr = await github.get_pull_request({ owner: 'company', repo: 'api', pull_number: 42 });
// Query deployment status from database (File)
const deployment = await db.get_deployment_status({ pr_id: pr.id });
// Send notification to Slack (HTTP)
await slack.post_message({
channel: '#releases',
text: \`PR #42 "\${pr.title}" deployed to \${deployment.environment}\`
});
return { pr: pr.title, environment: deployment.environment };
`);
Tools can dynamically discover and adapt to available interfaces:
const result = await client.callToolChain(`
// Discover available tools at runtime
console.log('Available interfaces:', __interfaces);
// Get specific tool interface for validation
const prInterface = __getToolInterface('github.get_pull_request');
console.log('PR tool expects:', prInterface);
// Use interface info for dynamic workflows
const hasSlackTools = __interfaces.includes('namespace slack');
if (hasSlackTools) {
await slack.post_message({ channel: '#dev', text: 'Analysis complete' });
}
return { toolsAvailable: hasSlackTools };
`);
Process large datasets without bloating the model's context:
const result = await client.callToolChain(`
// Fetch large dataset
const allIssues = await github.list_repository_issues({ owner: 'facebook', repo: 'react' });
console.log('Fetched', allIssues.length, 'total issues');
// Process efficiently in-sandbox
const criticalBugs = allIssues
.filter(issue => issue.labels.some(l => l.name === 'bug'))
.filter(issue => issue.labels.some(l => l.name === 'high priority'))
.map(issue => ({
number: issue.number,
title: issue.title,
author: issue.user.login,
daysOld: Math.floor((Date.now() - new Date(issue.created_at)) / (1000 * 60 * 60 * 24))
}))
.sort((a, b) => b.daysOld - a.daysOld);
// Only return processed summary (not 10,000 raw issues)
return {
totalIssues: allIssues.length,
criticalBugs: criticalBugs.slice(0, 10), // Top 10 oldest critical bugs
summary: \`Found \${criticalBugs.length} critical bugs, oldest is \${criticalBugs[0]?.daysOld} days old\`
};
`);
Built-in error handling with complete execution transparency:
const { result, logs } = await client.callToolChain(`
try {
console.log('Starting multi-step workflow...');
const data = await external_api.fetch_data({ id: 'user-123' });
console.log('Data fetched successfully');
const processed = await data_processor.transform(data);
console.warn('Processing completed with', processed.warnings.length, 'warnings');
return processed;
} catch (error) {
console.error('Workflow failed:', error.message);
throw error; // Propagates to outer error handling
}
`, 30000); // 30-second timeout
// Complete observability
console.log('Result:', result);
console.log('Execution logs:', logs);
// ['Starting multi-step workflow...', 'Data fetched successfully', '[WARN] Processing completed with 2 warnings']
Configure execution limits for different workload types:
// Quick operations (5 seconds)
const quickResult = await client.callToolChain(`return await ping.check();`, 5000);
// Heavy data processing (2 minutes)
const heavyResult = await client.callToolChain(`
const bigData = await database.export_full_dataset();
return await analytics.process_dataset(bigData);
`, 120000);
Plug-and-play with any AI framework. The built-in prompt template handles all the complexity:
import { CodeModeUtcpClient } from '@utcp/code-mode';
const systemPrompt = `
You are an AI assistant with access to tools via UTCP CodeMode.
${CodeModeUtcpClient.AGENT_PROMPT_TEMPLATE}
Additional instructions...
`;
// Works with any AI library
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [
{ role: 'system', content: systemPrompt },
{ role: 'user', content: 'Analyze the latest PR in microsoft/vscode' }
]
});
The template provides comprehensive guidance on:
searchTools → __interfaces → callToolChain)manual.tool() syntax)__getToolInterface())callToolChain(code: string, timeout?: number)Execute TypeScript code with full tool access and observability.
{result: any, logs: string[]} with execution result and captured console outputgetAllToolsTypeScriptInterfaces()Generate complete TypeScript interfaces for IDE integration.
searchTools(query: string) (from UtcpClient)Discover tools using natural language queries.
CodeModeUtcpClient.create(root_dir?, config?)Create a new client instance with optional configuration.
CodeModeUtcpClient.AGENT_PROMPT_TEMPLATEProduction-ready prompt template for AI agents.
Generate TypeScript definitions for full IntelliSense support:
# Generate tool interfaces
const interfaces = await client.getAllToolsTypeScriptInterfaces();
await fs.writeFile('generated-tools.d.ts', interfaces);
# Add to tsconfig.json
{
"compilerOptions": {
"typeRoots": ["./generated-tools.d.ts"]
}
}
Built-in observability for production deployments:
const { result, logs } = await client.callToolChain(userCode);
// Ship logs to your monitoring system
logs.forEach(log => {
if (log.startsWith('[ERROR]')) monitoring.error(log);
if (log.startsWith('[WARN]')) monitoring.warn(log);
});
The comprehensive Python study tested 16 realistic scenarios across:
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
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