Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Integrate OpenAI Agents SDK with You.com MCP server
Integrate OpenAI Agents SDK with You.com MCP server - Hosted and Streamable HTTP support for Python and TypeScript. Use when developer mentions OpenAI Agents SDK, OpenAI agents, or integrating OpenAI with MCP.
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Integrate OpenAI Agents SDK with You.com MCP server - Hosted and Streamable HTTP support for Python and TypeScript. Use when developer mentions OpenAI Agents SDK, OpenAI agents, or integrating OpenAI with MCP.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- EdwardIrby
- Source page
- View original listing
- Canonical URL
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Documentation
Integrate OpenAI Agents SDK with You.com MCP
Interactive workflow to set up OpenAI Agents SDK with You.com's MCP server.
Workflow
Ask: Language Choice Python or TypeScript? Ask: MCP Configuration Type Hosted MCP (OpenAI-managed with server URL): Recommended for simplicity Streamable HTTP (Self-managed connection): For custom infrastructure Install Package Python: pip install openai-agents TypeScript: npm install @openai/agents Ask: Environment Variables For Both Modes: YDC_API_KEY (You.com API key for Bearer token) OPENAI_API_KEY (OpenAI API key) Have they set them? If NO: Guide to get keys: YDC_API_KEY: https://you.com/platform/api-keys OPENAI_API_KEY: https://platform.openai.com/api-keys Ask: File Location NEW file: Ask where to create and what to name EXISTING file: Ask which file to integrate into (add MCP config) Create/Update File For NEW files: Use the complete template code from the "Complete Templates" section below User can run immediately with their API keys set For EXISTING files: Add MCP server configuration to their existing code Hosted MCP configuration block (Python): from agents import Agent, Runner from agents.mcp import HostedMCPTool # Validate: ydc_api_key = os.getenv("YDC_API_KEY") agent = Agent( name="Assistant", instructions="Use You.com tools to answer questions.", tools=[ HostedMCPTool( tool_config={ "type": "mcp", "server_label": "ydc", "server_url": "https://api.you.com/mcp", "headers": { "Authorization": f"Bearer {ydc_api_key}" }, "require_approval": "never", } ) ], ) Hosted MCP configuration block (TypeScript): import { Agent, hostedMcpTool } from '@openai/agents'; // Validate: const ydcApiKey = process.env.YDC_API_KEY; const agent = new Agent({ name: 'Assistant', instructions: 'Use You.com tools to answer questions.', tools: [ hostedMcpTool({ serverLabel: 'ydc', serverUrl: 'https://api.you.com/mcp', headers: { Authorization: `Bearer ${ydcApiKey}`, }, }), ], }); Streamable HTTP configuration block (Python): from agents import Agent, Runner from agents.mcp import MCPServerStreamableHttp # Validate: ydc_api_key = os.getenv("YDC_API_KEY") async with MCPServerStreamableHttp( name="You.com MCP Server", params={ "url": "https://api.you.com/mcp", "headers": {"Authorization": f"Bearer {ydc_api_key}"}, "timeout": 10, }, cache_tools_list=True, max_retry_attempts=3, ) as server: agent = Agent( name="Assistant", instructions="Use You.com tools to answer questions.", mcp_servers=[server], ) Streamable HTTP configuration block (TypeScript): import { Agent, MCPServerStreamableHttp } from '@openai/agents'; // Validate: const ydcApiKey = process.env.YDC_API_KEY; const mcpServer = new MCPServerStreamableHttp({ url: 'https://api.you.com/mcp', name: 'You.com MCP Server', requestInit: { headers: { Authorization: `Bearer ${ydcApiKey}`, }, }, }); const agent = new Agent({ name: 'Assistant', instructions: 'Use You.com tools to answer questions.', mcpServers: [mcpServer], });
Complete Templates
Use these complete templates for new files. Each template is ready to run with your API keys set.
Python Hosted MCP Template (Complete Example)
""" OpenAI Agents SDK with You.com Hosted MCP Python implementation with OpenAI-managed infrastructure """ import os import asyncio from agents import Agent, Runner from agents.mcp import HostedMCPTool # Validate environment variables ydc_api_key = os.getenv("YDC_API_KEY") openai_api_key = os.getenv("OPENAI_API_KEY") if not ydc_api_key: raise ValueError( "YDC_API_KEY environment variable is required. " "Get your key at: https://you.com/platform/api-keys" ) if not openai_api_key: raise ValueError( "OPENAI_API_KEY environment variable is required. " "Get your key at: https://platform.openai.com/api-keys" ) async def main(): """ Example: Search for AI news using You.com hosted MCP tools """ # Configure agent with hosted MCP tools agent = Agent( name="AI News Assistant", instructions="Use You.com tools to search for and answer questions about AI news.", tools=[ HostedMCPTool( tool_config={ "type": "mcp", "server_label": "ydc", "server_url": "https://api.you.com/mcp", "headers": { "Authorization": f"Bearer {ydc_api_key}" }, "require_approval": "never", } ) ], ) # Run agent with user query result = await Runner.run( agent, "Search for the latest AI news from this week" ) print(result.final_output) if __name__ == "__main__": asyncio.run(main())
Python Streamable HTTP Template (Complete Example)
""" OpenAI Agents SDK with You.com Streamable HTTP MCP Python implementation with self-managed connection """ import os import asyncio from agents import Agent, Runner from agents.mcp import MCPServerStreamableHttp # Validate environment variables ydc_api_key = os.getenv("YDC_API_KEY") openai_api_key = os.getenv("OPENAI_API_KEY") if not ydc_api_key: raise ValueError( "YDC_API_KEY environment variable is required. " "Get your key at: https://you.com/platform/api-keys" ) if not openai_api_key: raise ValueError( "OPENAI_API_KEY environment variable is required. " "Get your key at: https://platform.openai.com/api-keys" ) async def main(): """ Example: Search for AI news using You.com streamable HTTP MCP server """ # Configure streamable HTTP MCP server async with MCPServerStreamableHttp( name="You.com MCP Server", params={ "url": "https://api.you.com/mcp", "headers": {"Authorization": f"Bearer {ydc_api_key}"}, "timeout": 10, }, cache_tools_list=True, max_retry_attempts=3, ) as server: # Configure agent with MCP server agent = Agent( name="AI News Assistant", instructions="Use You.com tools to search for and answer questions about AI news.", mcp_servers=[server], ) # Run agent with user query result = await Runner.run( agent, "Search for the latest AI news from this week" ) print(result.final_output) if __name__ == "__main__": asyncio.run(main())
TypeScript Hosted MCP Template (Complete Example)
/** * OpenAI Agents SDK with You.com Hosted MCP * TypeScript implementation with OpenAI-managed infrastructure */ import { Agent, run, hostedMcpTool } from '@openai/agents'; // Validate environment variables const ydcApiKey = process.env.YDC_API_KEY; const openaiApiKey = process.env.OPENAI_API_KEY; if (!ydcApiKey) { throw new Error( 'YDC_API_KEY environment variable is required. ' + 'Get your key at: https://you.com/platform/api-keys' ); } if (!openaiApiKey) { throw new Error( 'OPENAI_API_KEY environment variable is required. ' + 'Get your key at: https://platform.openai.com/api-keys' ); } /** * Example: Search for AI news using You.com hosted MCP tools */ async function main() { // Configure agent with hosted MCP tools const agent = new Agent({ name: 'AI News Assistant', instructions: 'Use You.com tools to search for and answer questions about AI news.', tools: [ hostedMcpTool({ serverLabel: 'ydc', serverUrl: 'https://api.you.com/mcp', headers: { Authorization: `Bearer ${ydcApiKey}`, }, }), ], }); // Run agent with user query const result = await run( agent, 'Search for the latest AI news from this week' ); console.log(result.finalOutput); } main().catch(console.error);
TypeScript Streamable HTTP Template (Complete Example)
/** * OpenAI Agents SDK with You.com Streamable HTTP MCP * TypeScript implementation with self-managed connection */ import { Agent, run, MCPServerStreamableHttp } from '@openai/agents'; // Validate environment variables const ydcApiKey = process.env.YDC_API_KEY; const openaiApiKey = process.env.OPENAI_API_KEY; if (!ydcApiKey) { throw new Error( 'YDC_API_KEY environment variable is required. ' + 'Get your key at: https://you.com/platform/api-keys' ); } if (!openaiApiKey) { throw new Error( 'OPENAI_API_KEY environment variable is required. ' + 'Get your key at: https://platform.openai.com/api-keys' ); } /** * Example: Search for AI news using You.com streamable HTTP MCP server */ async function main() { // Configure streamable HTTP MCP server const mcpServer = new MCPServerStreamableHttp({ url: 'https://api.you.com/mcp', name: 'You.com MCP Server', requestInit: { headers: { Authorization: `Bearer ${ydcApiKey}`, }, }, }); try { // Connect to MCP server await mcpServer.connect(); // Configure agent with MCP server const agent = new Agent({ name: 'AI News Assistant', instructions: 'Use You.com tools to search for and answer questions about AI news.', mcpServers: [mcpServer], }); // Run agent with user query const result = await run( agent, 'Search for the latest AI news from this week' ); console.log(result.finalOutput); } finally { // Clean up connection await mcpServer.close(); } } main().catch(console.error);
Hosted MCP (Recommended)
What it is: OpenAI manages the MCP connection and tool routing through their Responses API. Benefits: โ Simpler configuration (no connection management) โ OpenAI handles authentication and retries โ Lower latency (tools run in OpenAI infrastructure) โ Automatic tool discovery and listing โ No need to manage async context or cleanup Use when: Building production applications Want minimal boilerplate code Need reliable tool execution Don't require custom transport layer Configuration: Python: from agents.mcp import HostedMCPTool tools=[ HostedMCPTool( tool_config={ "type": "mcp", "server_label": "ydc", "server_url": "https://api.you.com/mcp", "headers": { "Authorization": f"Bearer {os.environ['YDC_API_KEY']}" }, "require_approval": "never", } ) ] TypeScript: import { hostedMcpTool } from '@openai/agents'; tools: [ hostedMcpTool({ serverLabel: 'ydc', serverUrl: 'https://api.you.com/mcp', headers: { Authorization: `Bearer ${process.env.YDC_API_KEY}`, }, }), ]
Streamable HTTP MCP
What it is: You manage the MCP connection and transport layer yourself. Benefits: โ Full control over network connection โ Custom infrastructure integration โ Can add custom headers, timeouts, retry logic โ Run MCP server in your own environment โ Better for testing and development Use when: Need custom transport configuration Running MCP server in your infrastructure Require specific networking setup Development and testing scenarios Configuration: Python: from agents.mcp import MCPServerStreamableHttp async with MCPServerStreamableHttp( name="You.com MCP Server", params={ "url": "https://api.you.com/mcp", "headers": {"Authorization": f"Bearer {os.environ['YDC_API_KEY']}"}, "timeout": 10, }, cache_tools_list=True, max_retry_attempts=3, ) as server: agent = Agent(mcp_servers=[server]) TypeScript: import { MCPServerStreamableHttp } from '@openai/agents'; const mcpServer = new MCPServerStreamableHttp({ url: 'https://api.you.com/mcp', name: 'You.com MCP Server', requestInit: { headers: { Authorization: `Bearer ${process.env.YDC_API_KEY}`, }, }, }); await mcpServer.connect(); try { const agent = new Agent({ mcpServers: [mcpServer] }); // Use agent } finally { await mcpServer.close(); }
Available You.com Tools
After configuration, agents can discover and use: mcp__ydc__you_search - Web and news search mcp__ydc__you_express - AI-powered answers with web context mcp__ydc__you_contents - Web page content extraction
Environment Variables
Both API keys are required for both configuration modes: # Add to your .env file or shell profile export YDC_API_KEY="your-you-api-key-here" export OPENAI_API_KEY="your-openai-api-key-here" Get your API keys: You.com: https://you.com/platform/api-keys OpenAI: https://platform.openai.com/api-keys
Validation Checklist
Before completing: Package installed: openai-agents (Python) or @openai/agents (TypeScript) Environment variables set: YDC_API_KEY and OPENAI_API_KEY Template copied or configuration added to existing file MCP configuration type chosen (Hosted or Streamable HTTP) Authorization headers configured with Bearer token File is executable (Python) or can be compiled (TypeScript) Ready to test with example query
Testing Your Integration
Python: python your-file.py TypeScript: # With tsx (recommended for quick testing) npx tsx your-file.ts # Or compile and run tsc your-file.ts && node your-file.js
Common Issues
Install the package: # NPM npm install @openai/agents # Bun bun add @openai/agents # Yarn yarn add @openai/agents # pnpm pnpm add @openai/agents Set your You.com API key: export YDC_API_KEY="your-api-key-here" Get your key at: https://you.com/platform/api-keys Set your OpenAI API key: export OPENAI_API_KEY="your-api-key-here" Get your key at: https://platform.openai.com/api-keys Verify your YDC_API_KEY is valid: Check the key at https://you.com/platform/api-keys Ensure no extra spaces or quotes in the environment variable Verify the Authorization header format: Bearer ${YDC_API_KEY} For Both Modes: Ensure server_url: "https://api.you.com/mcp" is correct Verify Authorization header includes Bearer prefix Check YDC_API_KEY environment variable is set Confirm require_approval is set to "never" for automatic execution For Streamable HTTP specifically: Ensure MCP server is connected before creating agent Verify connection was successful before running agent For Streamable HTTP only: Increase timeout or retry attempts: Python: async with MCPServerStreamableHttp( params={ "url": "https://api.you.com/mcp", "headers": {"Authorization": f"Bearer {os.environ['YDC_API_KEY']}"}, "timeout": 30, # Increased timeout }, max_retry_attempts=5, # More retries ) as server: # ... TypeScript: const mcpServer = new MCPServerStreamableHttp({ url: 'https://api.you.com/mcp', requestInit: { headers: { Authorization: `Bearer ${process.env.YDC_API_KEY}` }, // Add custom timeout via fetch options }, });
Additional Resources
OpenAI Agents SDK (Python): https://openai.github.io/openai-agents-python/ OpenAI Agents SDK (TypeScript): https://openai.github.io/openai-agents-js/ MCP Configuration (Python): https://openai.github.io/openai-agents-python/mcp/ MCP Configuration (TypeScript): https://openai.github.io/openai-agents-js/guides/mcp/ You.com MCP Server: https://documentation.you.com/developer-resources/mcp-server API Keys: You.com: https://you.com/platform/api-keys OpenAI: https://platform.openai.com/api-keys
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs
- SKILL.md