Openai Deep Research Mcp

Created By
fbettaga year ago
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 iterative exploration, automatic knowledge gap identification, and seamless integration with OpenAI's Deep Research models for scholarly-quality research automation.
Overview

What is OpenAI Deep Research MCP?

OpenAI Deep Research MCP is a server that enables AI assistants to conduct comprehensive, multi-step research through intelligent web search and content synthesis, transforming complex research queries into structured, citation-backed reports.

How to use OpenAI Deep Research MCP?

To use the server, run the command OPENAI_API_KEY={OPENAI_API_KEY} npx github:fbettag/openai-deep-research-mcp after setting your OpenAI API key.

Key features of OpenAI Deep Research MCP?

  • Multi-step exploration with automatic knowledge gap identification.
  • Comprehensive content extraction with enhanced web scraping.
  • Smart synthesis of multiple sources into coherent reports.
  • Proper citation management with numbered references.

Use cases of OpenAI Deep Research MCP?

  1. Academic research for literature reviews and thesis research.
  2. Business intelligence for market analysis and competitive research.
  3. Content creation for in-depth articles and comprehensive guides.
  4. Policy research for evidence-based recommendations.
  5. Technical documentation for API research and technology comparisons.

FAQ from OpenAI Deep Research MCP?

  • Can this server handle all types of research?

Yes! It is designed for various research types including academic, business, and technical documentation.

  • Is there a cost associated with using OpenAI Deep Research MCP?

The server is free to use, but requires an OpenAI API key for access.

  • How does it ensure citation accuracy?

The server automatically manages citations and provides numbered references for all sourced content.

Server Config

{
  "mcpServers": {
    "openai-deep-research": {
      "command": "npx",
      "args": [
        "github:fbettag/openai-deep-research-mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "sk-your-openai-api-key-here"
      }
    }
  }
}
Project Info
Created At
a year ago
Updated At
a year ago
Author Name
fbettag
Star
-
Language
-
License
-

Recommend Servers

View All
Mnemom

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

13 hours ago
Docwand

13 hours ago