Krishna Kumar 9a6ac3fd2f Add OpenAI Deep Research MCP server
- FastMCP server with deep_research and deep_research_info tools
- OpenAI Responses API integration with background polling
- Configurable model via DEEP_RESEARCH_MODEL env var
- Default: o4-mini-deep-research (faster/cheaper)
- Optional FastAPI backend for standalone use
- Tested successfully: 80s query, 20 web searches, 4 citations
2025-12-30 16:00:37 -06:00
2025-12-30 16:00:37 -06:00
2025-12-30 16:00:37 -06:00

MCP Deep Research

MCP Server for OpenAI Deep Research API - comprehensive web research with citations.

Overview

This MCP server provides access to OpenAI's Deep Research models, which can:

  • Perform extensive web searches
  • Analyze data with code execution
  • Synthesize findings into structured reports
  • Provide citations for all sources

Installation

cd mcp-deep-research
uv sync

Configuration

Environment Variables

Variable Required Default Description
OPENAI_API_KEY Yes - Your OpenAI API key
DEEP_RESEARCH_MODEL No o4-mini-deep-research-2025-06-26 Research model to use
DEEP_RESEARCH_POLL_INTERVAL No 5.0 Seconds between status polls

Available Models

  • o4-mini-deep-research-2025-06-26 - Faster, cheaper (DEFAULT)
  • o3-deep-research-2025-06-26 - More thorough, ~$1+ per query

Usage

As MCP Server (stdio)

OPENAI_API_KEY=your-key uv run python -m mcp_server.server

Standalone FastAPI (optional)

OPENAI_API_KEY=your-key uv run python -m backend.main

Runs on http://localhost:8002 by default.

MCP Tools

deep_research

Performs comprehensive web research on a query.

Parameters:

  • query (required): The research question or topic
  • system_prompt (optional): Instructions to guide research focus
  • include_code_analysis (optional, default: true): Allow code execution for data analysis
  • max_wait_minutes (optional, default: 15): Maximum time to wait

Returns:

{
  "status": "completed",
  "model": "o4-mini-deep-research-2025-06-26",
  "report_text": "# Research Report\n\n...",
  "citations": [
    {"title": "Source Title", "url": "https://..."}
  ],
  "web_searches": 12,
  "code_executions": 2,
  "elapsed_time": 180.5
}

deep_research_info

Returns configuration information about the deep research setup.

Integration with CouncilApp

The server is configured in councilapp.backend/packages/server/src/server/session.ts:

"deep-research": {
  command: "/bin/bash",
  args: ["-c", `cd ${MCP_DEEP_RESEARCH_PATH} && uv run python -m mcp_server.server`],
  env: {
    OPENAI_API_KEY: process.env.OPENAI_API_KEY,
    DEEP_RESEARCH_MODEL: process.env.DEEP_RESEARCH_MODEL,
  },
}

Docker Configuration

Set these in your Docker environment or docker-compose.yml:

environment:
  - OPENAI_API_KEY=sk-...
  - DEEP_RESEARCH_MODEL=o4-mini-deep-research-2025-06-26  # or o3-deep-research-2025-06-26

Pricing

Deep research costs vary based on:

  • Number of web searches performed
  • Code interpreter usage
  • Token consumption

Approximate costs:

  • o4-mini: Lower cost, faster responses
  • o3: ~$1+ per complex query with many web searches

Test Results (2024-12-30)

Successfully tested with query: "What is the current population of Tokyo in 2024?"

Status: completed
Model: o4-mini-deep-research-2025-06-26
Elapsed time: 80.5s
Web searches: 20
Citations: 4

Report excerpt:
# Tokyo Population (2024)
As of late 2024, the official population of Tokyo Metropolis is about 14.2 million people.

License

MIT

Description
MCP Server for OpenAI Deep Research API
Readme 89 KiB
Languages
Python 100%