A
licenseNot graded
qualityD
maintenanceEnables AI assistants to perform up-to-date web searches through the Tavily API, providing comprehensive search results with AI-generated summaries.
7
MIT
OpenAI WebSearch MCP Server
by fastmcp-me
Provides intelligent web search capabilities using OpenAI's reasoning models, enabling AI assistants to perform searches with customizable models and reasoning effort.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OpenAI WebSearch MCP Serversearch for the latest AI breakthroughs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OpenAI WebSearch MCP Server 🔍
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.
✨ Features
🧠 Reasoning Model Support: Full compatibility with OpenAI's latest reasoning models (gpt-5, gpt-5-mini, gpt-5-nano, o3, o4-mini)
⚡ Smart Effort Control: Intelligent
reasoning_effortdefaults based on use case🔄 Multi-Mode Search: Fast iterations with gpt-5-mini or deep research with gpt-5
🌍 Localized Results: Support for location-based search customization
📝 Rich Descriptions: Complete parameter documentation for easy integration
🔧 Flexible Configuration: Environment variable support for easy deployment
Related MCP server: OpenAI WebSearch MCP Server
🚀 Quick Start
One-Click Installation for Claude Desktop
OPENAI_API_KEY=sk-xxxx uvx --with openai-websearch-mcp openai-websearch-mcp-installReplace sk-xxxx with your OpenAI API key from the OpenAI Platform.
⚙️ Configuration
Claude Desktop
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"
}
}
}
}Cursor
Add to your MCP settings in Cursor:
Open Cursor Settings (
Cmd/Ctrl + ,)Search for "MCP" or go to Extensions → MCP
Add server configuration:
{
"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
Claude Code automatically detects MCP servers configured for Claude Desktop. Use the same configuration as above for Claude Desktop.
Local Development
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"
}
}
}
}🛠️ Available Tools
openai_web_search
Intelligent web search with reasoning model support.
Parameters
Parameter | Type | Description | Default |
|
| The search query or question to search for | Required |
|
| AI model to use. Supports gpt-4o, gpt-4o-mini, gpt-5, gpt-5-mini, gpt-5-nano, o3, o4-mini |
|
|
| Reasoning effort level: low, medium, high, minimal | Smart default |
|
| Web search API version |
|
|
| Context amount: low, medium, high |
|
|
| Optional location for localized results |
|
💬 Usage Examples
Once configured, simply ask your AI assistant to search for information using natural language:
Quick Search
"Search for the latest developments in AI reasoning models using openai_web_search"
Deep Research
"Use openai_web_search with gpt-5 and high reasoning effort to provide a comprehensive analysis of quantum computing breakthroughs"
Localized Search
"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.
🤖 Model Selection Guide
Quick Multi-Round Searches 🚀
Recommended:
gpt-5-miniwithreasoning_effort: "low"Use Case: Fast iterations, real-time information, multiple quick queries
Benefits: Lower latency, cost-effective for frequent searches
Deep Research 🔬
Recommended:
gpt-5withreasoning_effort: "medium"or"high"Use Case: Comprehensive analysis, complex topics, detailed investigation
Benefits: Multi-round reasoned results, no need for agent iterations
Model Comparison
Model | Reasoning | Default Effort | Best For |
| ❌ | N/A | Standard search |
| ❌ | N/A | Basic queries |
| ✅ |
| Fast iterations |
| ✅ |
| Deep research |
| ✅ |
| Balanced approach |
| ✅ |
| Advanced reasoning |
| ✅ |
| Efficient reasoning |
📦 Installation
Using uvx (Recommended)
# Install and run directly
uvx openai-websearch-mcp
# Or install globally
uvx install openai-websearch-mcpUsing pip
# Install from PyPI
pip install openai-websearch-mcp
# Run the server
python -m openai_websearch_mcpFrom Source
# 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👩💻 Development
Setup Development Environment
# 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 .Environment Variables
Variable | Description | Default |
| Your OpenAI API key | Required |
| Default model to use |
|
🐛 Debugging
Using MCP Inspector
# For uvx installations
npx @modelcontextprotocol/inspector uvx openai-websearch-mcp
# For pip installations
npx @modelcontextprotocol/inspector python -m openai_websearch_mcpCommon Issues
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.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
🤖 Generated with Claude Code
🔥 Powered by OpenAI's Web Search API
🛠️ Built on the Model Context Protocol
Co-Authored-By: Claude noreply@anthropic.com
Available Tools
1 toolopenai_web_searchA
OpenAI Web Search with reasoning models.
For quick multi-round searches: Use 'gpt-5-mini' with reasoning_effort='low' for fast iterations.
For deep research: Use 'gpt-5' with reasoning_effort='medium' or 'high'. The result is already multi-round reasoned, so agents don't need continuous iterations.
Supports: gpt-4o (no reasoning), gpt-5/gpt-5-mini/gpt-5-nano, o3/o4-mini (with reasoning).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Web search API version to use | web_search_preview |
| input | Yes | The search query or question to search for | |
| model | No | AI model to use. Defaults to OPENAI_DEFAULT_MODEL env var or gpt-5-mini | |
| user_location | No | Optional user location for localized search results | |
| reasoning_effort | No | Reasoning effort level for supported models (gpt-5, o3, o4-mini). Default: low for gpt-5-mini, medium for others | |
| search_context_size | No | Amount of context to include in search results | medium |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the result is 'already multi-round reasoned, so agents don't need continuous iterations', providing useful behavioral insight about the reasoning process. However, it doesn't mention potential rate limits, API requirements, or other operational characteristics. Since web search is inherently read-only, that aspect is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with the main purpose stated in the first line. It then provides two clear usage scenarios and a list of supported models. Every sentence earns its place, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has a rich schema and an output schema (which explains return values), the description doesn't need to cover those. It adequately covers the critical model selection guidance and reasoning effort recommendations, which are the non-obvious parts of using this tool. It's complete enough for an agent to decide when and how to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already includes descriptions for all 6 parameters (100% coverage), so the baseline is 3. The description adds extra meaning by explaining the relationship between models and reasoning_effort for different use cases, which goes beyond the schema's per-parameter descriptions. This helps the agent select appropriate model/effort combinations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'OpenAI Web Search' which indicates the tool performs web searches, and mentions reasoning models. It doesn't use a specific verb like 'search' but the name and description together make the purpose clear. No sibling tools exist to differentiate from, so it doesn't need to distinguish itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance for different usage scenarios: 'For quick multi-round searches: Use gpt-5-mini with reasoning_effort='low'' and 'For deep research: Use gpt-5 with reasoning_effort='medium' or 'high''. This provides clear context on when to use specific model settings, though it doesn't mention when not to use the tool since there are no sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
A4.2/5.0
Disambiguation5/5
There is only one tool, so there is no possibility of confusion or overlapping purposes. The tool's purpose is clear and singular.
Naming Consistency5/5
With a single tool, the naming is inherently consistent. 'openai_web_search' follows a clear verb_noun pattern and clearly indicates its function.
Tool Count4/5
The server is dedicated to OpenAI web search, and a single tool fully covers this narrow purpose. While under the typical 3-15 range, it is not excessive or trivial, making the count reasonable for the scope.
Completeness5/5
For a web search server, the tool provides comprehensive functionality, handling multiple models, reasoning efforts, and multi-round searches. There are no obvious missing capabilities for the stated purpose.
Maintenance
ActivityInactive
ResponsivenessSyncing
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