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Openai Deep Research MCP Server

@fbettag

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

fbettag

提交者

Franz Bettag

关于 Openai Deep Research MCP Server

OpenAI Deep Research MCP Server enables AI assistants to conduct comprehensive, multi-step research through intelligent web search and content synthesis. Transforms complex research queries into structured, citation-backed reports without writing custom search logic. Features ite

#research#deep-research#web-search#openai#natural-language#report-generation#knowledge-synthesis#citations#real-time#automation#academic#analysis

Openai Deep Research MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "openai-deep-research": {
      "command": "npx",
      "args": [
        "github:fbettag/openai-deep-research-mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "sk-your-openai-api-key-here"
      }
    }
  }
}

Openai Deep Research MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Openai Deep Research Mcp?

Openai Deep Research Mcp is a Model Context Protocol (MCP) server that brings OpenAI’s Deep Research capabilities to AI applications through standardized search and fetch operations. It enables AI assistants to perform comprehensive, multi-step research on any topic, generating scholarly reports with proper citations.

How to use Openai Deep Research Mcp?

Install the server using npx with the command npx github:fbettag/openai-deep-research-mcp. Then configure it with your OpenAI API key and add it to your MCP client (such as Claude Desktop or Cursor) following the client’s documentation.

Key features of Openai Deep Research Mcp

  • Multi-step exploration that identifies knowledge gaps automatically
  • Comprehensive web scraping with structured data extraction
  • Smart synthesis combining multiple sources into coherent reports
  • Proper academic citations with numbered references
  • MCP standard compliance for client interoperability
  • SSE transport enabling real-time streaming responses
  • Token optimization to work within LLM context limits
  • Error resilience with graceful failure handling and partial reports

Use cases of Openai Deep Research Mcp

  • Academic Research: literature reviews, thesis research, scholarly analysis
  • Business Intelligence: market analysis, competitive research, trend identification
  • Content Creation: in-depth articles, fact-checking, comprehensive guides
  • Policy Research: evidence-based recommendations, regulatory analysis
  • Technical Documentation: API research, technology comparisons, implementation guides

常见问题

1

What does “Deep Research” mean in this context?

It refers to multi-step exploration that automatically identifies knowledge gaps, generates focused search queries, extracts content, and synthesizes multiple sources into structured reports with numbered citations.

2

Which MCP clients are supported?

The server works seamlessly with Claude Desktop, Cursor, and other MCP clients that follow the Model Context Protocol standard.

3

How does the server handle errors?

It includes error resilience mechanisms that allow graceful handling of failures and partial report generation when some steps cannot complete.

4

What transport does the server use?

It uses SSE (Server-Sent Events) transport for real-time streaming of research progress and results.

5

Are there any token or context limitations handled?

Yes, the server includes token optimization and intelligent content management to work within LLM context limits, ensuring reports remain coherent and complete.

评论

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