このMCPサーバーは、モデルコンテキストプロトコル（MCP）を介してOpenAIのウェブ検索機能へのアクセスを提供します。AIアシスタントは、ユーザーとの会話中にウェブを検索し、アシスタントの学習データには含まれていない可能性のある最新情報を提供できます。このサーバーは、Claude.appまたはZedエディタで使用できるようにインストールおよび設定できます。
!!このコマンドを使うとconfigureファイルを自動更新できます（推奨）
OPENAI_API_KEY=sk-xxxx uv run --with uv --with openai-websearch-mcp openai-websearch-mcp-installsk-xxxxはAPIキーです。OpenAIのオープンプラットフォームから取得できます。
近日公開
近日公開
Related MCP server: SearchAPI MCP Server
web_search - openai websearch をツールとして呼び出します。
必要な引数:
type （文字列）：web_search_preview
search_context_size (文字列): 検索に使用するコンテキストウィンドウのスペース量に関する概要ガイドライン。low、medium、high のいずれかです。デフォルトは medium です。
user_location （オブジェクトまたはnull）
type (文字列): 位置情報の種類 > 近似値。常に近似値となります。
city (文字列): ユーザーの都市のフリーテキスト入力 (例: San Francisco)。
country (文字列): ユーザーの 2 文字の ISO 国コード (例: US)。
region (文字列): ユーザーの地域を自由に入力するテキスト (例: カリフォルニア)。
timezone (文字列): ユーザーの IANA タイムゾーン (例: America/Los_Angeles)。
インストール前にuvxがインストールされていることを確認してください
Claude 設定に追加:
1、uvxの使用
"mcpServers": {
"openai-websearch-mcp": {
"command": "uvx",
"args": ["openai-websearch-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
}2、pipインストールを使用する
1）pipでopenai-websearch-mcpをインストールします。
pip install openai-websearch-mcp2）Claudeの設定を変更する
"mcpServers": {
"openai-websearch-mcp": {
"command": "python",
"args": ["-m", "openai_websearch_mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
}Zed のsettings.jsonに追加します:
uvxの使用
"context_servers": [
"openai-websearch-mcp": {
"command": "uvx",
"args": ["openai-websearch-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
],pipインストールの使用
"context_servers": {
"openai-websearch-mcp": {
"command": "python",
"args": ["-m", "openai_websearch_mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
},MCPインスペクタを使用してサーバーをデバッグできます。UVXインストールの場合：
npx @modelcontextprotocol/inspector uvx openai-websearch-mcpOpenAI 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).
| Name | Required | Description | Default |
|---|---|---|---|
| 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 | |
| reasoning_effort | No | Reasoning effort level for supported models (gpt-5, o3, o4-mini). Default: low for gpt-5-mini, medium for others | |
| type | No | Web search API version to use | web_search_preview |
| search_context_size | No | Amount of context to include in search results | medium |
| user_location | No | Optional user location for localized search results |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0openai_web_searchweb_searchweb_searchScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose of performing web searches with OpenAI models.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'openai_web_search' follows a clear and descriptive pattern that would be consistent if more tools existed.
A single tool is generally too few for a server's purpose, as it limits functionality and flexibility. While the tool is well-described, the server's scope appears to be web search, which could benefit from additional tools for filtering, refining, or managing searches.
The server is severely incomplete for web search functionality. It lacks essential operations such as filtering results, handling pagination, saving or retrieving search history, or configuring search parameters beyond model selection. This will likely cause agent failures in complex search tasks.
One API key for 6 AI models. Pay-per-use. MCP protocol support with web search.
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