by conechoai
OpenAI WebSearch enables real-time AI search using Bing AI by Microsoft for up-to-date web info and configurable search
Enables AI assistants to search the web in real-time using OpenAI's search functionality to retrieve current information beyond their training data cutoffs.
OpenAI WebSearch is a community-built MCP server published by conechoai that provides AI assistants with tools and capabilities via the Model Context Protocol. OpenAI WebSearch enables real-time AI search using Bing AI by Microsoft for up-to-date web info and configurable search It is categorized under search web.
You can install OpenAI WebSearch in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
OpenAI WebSearch is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Fetch and extract information from websites automatically
Example
Research competitor pricing, scrape product reviews, monitor news mentions
Automate 5-10 hours/week of manual web research
Track website changes, new content, price updates
Example
Monitor competitor blog for new posts, track stock availability, watch for pricing changes
Stay informed without manual checking, never miss important updates
Extract structured data from multiple websites
Example
Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data
Build datasets 100x faster than manual copying
Share your MCP server with the developer community
OpenAI WebSearch is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
OpenAI WebSearch is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired OpenAI WebSearch into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend OpenAI WebSearch for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, OpenAI WebSearch benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
OpenAI WebSearch reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
OpenAI WebSearch has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: OpenAI WebSearch is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated OpenAI WebSearch against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: OpenAI WebSearch surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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An advanced MCP server that provides intelligent web search capabilities using OpenAI's reasoning models. Perfect for AI assistants that need up-to-date information with smart reasoning capabilities.
reasoning_effort defaults based on use caseOPENAI_API_KEY=sk-xxxx uvx --with openai-websearch-mcp openai-websearch-mcp-install
Replace sk-xxxx with your OpenAI API key from the OpenAI Platform.
Add to your claude_desktop_config.json:
{
"mcpServers": {
"openai-websearch-mcp": {
"command": "uvx",
"args": ["openai-websearch-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here",
"OPENAI_DEFAULT_MODEL": "gpt-5-mini"
}
}
}
}
Add to your MCP settings in Cursor:
Cmd/Ctrl + ,){
"mcpServers": {
"openai-websearch-mcp": {
"command": "uvx",
"args": ["openai-websearch-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here",
"OPENAI_DEFAULT_MODEL": "gpt-5-mini"
}
}
}
}
Claude Code automatically detects MCP servers configured for Claude Desktop. Use the same configuration as above for Claude Desktop.
For local testing, use the absolute path to your virtual environment:
{
"mcpServers": {
"openai-websearch-mcp": {
"command": "/path/to/your/project/.venv/bin/python",
"args": ["-m", "openai_websearch_mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here",
"OPENAI_DEFAULT_MODEL": "gpt-5-mini",
"PYTHONPATH": "/path/to/your/project/src"
}
}
}
}
openai_web_searchIntelligent web search with reasoning model support.
| Parameter | Type | Description | Default |
|---|---|---|---|
input | string | The search query or question to search for | Required |
model | string | AI model to use. Supports gpt-4o, gpt-4o-mini, gpt-5, gpt-5-mini, gpt-5-nano, o3, o4-mini | gpt-5-mini |
reasoning_effort | string | Reasoning effort level: low, medium, high, minimal | Smart default |
type | string | Web search API version | web_search_preview |
search_context_size | string | Context amount: low, medium, high | medium |
user_location | object | Optional location for localized results | null |
Once configured, simply ask your AI assistant to search for information using natural language:
"Search for the latest developments in AI reasoning models using openai_web_search"
"Use openai_web_search with gpt-5 and high reasoning effort to provide a comprehensive analysis of quantum computing breakthroughs"
"Search for local tech meetups in San Francisco this week using openai_web_search"
The AI assistant will automatically use the openai_web_search tool with appropriate parameters based on your request.
gpt-5-mini with reasoning_effort: "low"gpt-5 with reasoning_effort: "medium" or "high"| Model | Reasoning | Default Effort | Best For |
|---|---|---|---|
gpt-4o | ❌ | N/A | Standard search |
gpt-4o-mini | ❌ | N/A | Basic queries |
gpt-5-mini | ✅ | low | Fast iterations |
gpt-5 | ✅ | medium | Deep research |
gpt-5-nano | ✅ | medium | Balanced approach |
o3 | ✅ | medium | Advanced reasoning |
o4-mini | ✅ | medium | Efficient reasoning |
# Install and run directly
uvx openai-websearch-mcp
# Or install globally
uvx install openai-websearch-mcp
# Install from PyPI
pip install openai-websearch-mcp
# Run the server
python -m openai_websearch_mcp
# Clone the repository
git clone https://github.com/yourusername/openai-websearch-mcp.git
cd openai-websearch-mcp
# Install dependencies
uv sync
# Run in development mode
uv run python -m openai_websearch_mcp
# Clone and setup
git clone https://github.com/yourusername/openai-websearch-mcp.git
cd openai-websearch-mcp
# Create virtual environment and install dependencies
uv sync
# Run tests
uv run python -m pytest
# Install in development mode
uv pip install -e .
| Variable | Description | Default |
|---|---|---|
OPENAI_API_KEY | Your OpenAI API key | Required |
OPENAI_DEFAULT_MODEL | Default model to use | gpt-5-mini |
# For uvx installations
npx @modelcontextprotocol/inspector uvx openai-websearch-mcp
# For pip installations
npx @modelcontextprotocol/inspector python -m openai_websearch_mcp
Issue: "Unsupported parameter: 'reasoning.effort'" Solution: This occurs when using non-reasoning models (gpt-4o, gpt-4o-mini) with reasoning_effort parameter. The server automatically handles this by only applying reasoning parameters to compatible models.
Issue: "No module named 'openai_websearch_mcp'" Solution: Ensure you've installed the package correctly and your Python path includes the package location.
This project is licensed under the MIT License - see the LICENSE file for details.
Co-Authored-By: Claude [email protected]
Interact with services that don't offer APIs
Example
Check form submissions, validate website functionality, test user flows
Automate interactions with any website, even without API
Prerequisites
Time Estimate
20-40 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.
Protocols
Compatibility
✓ Use when
Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.
✗ Avoid when
Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.