Which integrations are available for this server?

Enables comprehensive web research using Google's Gemini Deep Research Agent via the MCP server.

How do I use gemini-deep-research-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@gemini-deep-research-mcp research the latest developments in quantum computing and their potential impact on cryptography" 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.

gemini-deep-research-mcp

by bharatvansh

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Gemini Deep Research MCP

An MCP server that exposes Gemini's Deep Research Agent for comprehensive web research.

One-Click Install

IDE	Install
Cursor
VS Code
VS Code Insiders

Note: After clicking, replace your-api-key with your Gemini API key. VS Code requires version 1.101+.

Related MCP server: Gemini Research MCP Server

Installation Methods

Using npx (Node.js)

Requires Node.js 16+ and uv.

npx @bharatvansh/gemini-deep-research-mcp

{
  "servers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json (macOS/Linux) or %USERPROFILE%\.codeium\windsurf\mcp_config.json (Windows):

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codex/config.toml:

[mcp_servers.gemini-deep-research]
command = "npx"
args = ["-y", "@bharatvansh/gemini-deep-research-mcp"]

[mcp_servers.gemini-deep-research.env]
GEMINI_API_KEY = "your-api-key"

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "npx",
      "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to your Antigravity mcp_config.json:

{
  "gemini-deep-research": {
    "command": "npx",
    "args": ["-y", "@bharatvansh/gemini-deep-research-mcp"],
    "env": {
      "GEMINI_API_KEY": "your-api-key"
    }
  }
}

Using uvx (Python)

Requires uv.

uvx gemini-deep-research-mcp

{
  "servers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json (macOS/Linux) or %USERPROFILE%\.codeium\windsurf\mcp_config.json (Windows):

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codex/config.toml:

[mcp_servers.gemini-deep-research]
command = "uvx"
args = ["gemini-deep-research-mcp"]

[mcp_servers.gemini-deep-research.env]
GEMINI_API_KEY = "your-api-key"

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "uvx",
      "args": ["gemini-deep-research-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to your Antigravity mcp_config.json:

{
  "gemini-deep-research": {
    "command": "uvx",
    "args": ["gemini-deep-research-mcp"],
    "env": {
      "GEMINI_API_KEY": "your-api-key"
    }
  }
}

Using pip

pip install gemini-deep-research-mcp

{
  "servers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json (macOS/Linux) or %USERPROFILE%\.codeium\windsurf\mcp_config.json (Windows):

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to ~/.codex/config.toml:

[mcp_servers.gemini-deep-research]
command = "gemini-deep-research-mcp"

[mcp_servers.gemini-deep-research.env]
GEMINI_API_KEY = "your-api-key"

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "gemini-deep-research": {
      "command": "gemini-deep-research-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to your Antigravity mcp_config.json:

{
  "gemini-deep-research": {
    "command": "gemini-deep-research-mcp",
    "env": {
      "GEMINI_API_KEY": "your-api-key"
    }
  }
}

Antigravity

Open the Agent side panel → click ... → MCP Store
Search for your MCP server or click Add Custom Server
Add this configuration to your mcp_config.json:

{
  "gemini-deep-research": {
    "command": "uvx",
    "args": ["gemini-deep-research-mcp"],
    "env": {
      "GEMINI_API_KEY": "your-api-key"
    }
  }
}

Prerequisites

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Tools

1. `start_deep_research`

Initiates a deep, multi-step web research job in the background using Google's Deep Research Agent. Returns a job_id, which you can use to check the status of completion using check_deep_research(job_id=...).

Parameter	Type	Required	Default	Description
`prompt`	string	✓	—	Your comprehensive research question or topic

Output	Description
`job_id`	Unique tracking ID for the research job
`status`	Initial job state (e.g. `in_progress`)

2. `check_deep_research`

Checks the status of a Deep Research job using its job_id and returns the complete report once finished.

Parameter	Type	Required	Default	Description
`job_id`	string	✓	—	The research tracking ID from `start_deep_research`
`include_citations`	boolean		`true`	Include source URLs in the report

Output	Description
`job_id`	The tracking ID of the research job
`status`	Exact current job state (`in_progress`, `completed`, `failed`, or `cancelled`)
`report_text`	Synthesized markdown research report (when `completed`)
`uptime`	Elapsed time while the job is in progress, when available
`error`	Failure details, when the API provides them

Configuration

Variable	Required	Default	Description
`GEMINI_API_KEY`	✓	—	Your Gemini API key
`GEMINI_DEEP_RESEARCH_AGENT`		`deep-research-preview-04-2026`	Deep Research agent to use. Set to `deep-research-max-preview-04-2026` for maximum thoroughness, or `deep-research-preview-04-2026` for standard speed.

Development

git clone https://github.com/bharatvansh/gemini-deep-research-mcp.git
cd gemini-deep-research-mcp
pip install -e .[dev]
pytest

License

MIT

Available Tools

2 tools

check_deep_researchCheck Gemini Deep ResearchA

Read-onlyIdempotent

Checks the status of a Deep Research job using its job_id and returns the complete report once finished.

Parameters:

job_id: The tracking ID returned by start_deep_research (required)
include_citations: Whether to include source URLs in the report (default: true)

Returns:

job_id: The tracking ID of the research job
status: Current job state ('in_progress', 'completed', 'failed', or 'cancelled')
report_text: The synthesized research report when completed
uptime: Elapsed time while the job is in progress, when available
error: Failure details, when the API provides them

Inputs: job_id (required), include_citations. Returns: error, job_id, status, uptime, +1 more

start_deep_researchStart Gemini Deep ResearchA

Initiates a deep, multi-step web research job in the background using Google's Deep Research Agent. Returns a job_id, which you can use to check the status of completion using check_deep_research(job_id=...).

Parameters:

prompt: The comprehensive research question or topic to investigate (required)

Returns:

job_id: Unique tracking ID for the research job
status: Initial job state (typically 'in_progress')

Inputs: prompt (required). Returns: job_id, status

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored 2026-08-22 11:31 across 2 tools

Disambiguation5/5

The two tools have completely distinct responsibilities: one initiates a research job accordion to a prompt, and the other polls for status and retrieves the report. There is no overlap in functionality or ambiguity about which tool to use in any given situation.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern: 'start_deep_research' and 'check_deep_research'. The action (start/check) clearly precedes the domain (deep_research), making the naming predictable and intuitive.

Tool Count3/5

With only two tools, the surface is minimal but matches the narrow scope of managing a deep research job. However, per the calibration this falls in the borderline category (1-2 tools). It is slightly thin, though not unreasonable for such a specific workflow.

Completeness4/5

The lifecycle is essentially complete: start a job and retrieve the final report. The absence of a cancel or list tools is a minor gap, but the core workflow (initiate and poll) is fully covered from the agent's perspective.

Maintenance

ActivityNo data

ResponsivenessOutdated data

Resources

Related MCP Connectors

SearchPipeA
tech.searchpipe
Web search API and MCP server for developers building AI agents.
Search Browser Automation Agent Orchestration
31
https://searchpipe.tech/mcp/
Parallel Task MCPA
ai.parallel
An MCP server for deep research or task groups
Autonomous Agents Agent Orchestration Research & Data
4416
https://task-mcp.parallel.ai/mcp
driflyte-mcp-server
com.driflyte
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
Search Web Scraping Knowledge & Memory
211
https://mcp.driflyte.com/openai
greadA
io.github.NitroRCr
An MCP server that gives your AI access to the source code and docs of all public github repos
Version Control Code Analysis Documentation Access
5365
https://api.gread.dev/mcp

Related MCP Servers

Langgraph Deep Search MCP
Search RAG Systems Autonomous Agents
RyotaOzawa0
A
license
Not graded
quality
D
maintenance
An MCP server that utilizes LangGraph and Google Gemini to conduct comprehensive research through multi-iteration deep searches and quick results. It provides high-quality analysis with automated citations and grounding metadata for thorough investigations.
Updated a year ago (2025-06-26 17:45 UTC)
2
MIT
Gemini Research MCP Server
Search Research & Data AI & Machine Learning
machinemates-ai
A
license
A
quality
B
maintenance
MCP server for AI-powered research using Gemini. Provides fast grounded web search, deep autonomous research, URL extraction, and session management.
Updated 19 days ago (2026-10-23 23:13 UTC)
9
108 PyPI
9
MIT
Gemini Agent MCP Server
Search AI & Machine Learning Code Execution
cybaea
A
license
A
quality
C
maintenance
An advanced MCP server that provides an agentic interface to Google's Gemini 3.1 models via Vertex AI, combining real-time Google Search, URL analysis, and Python code execution to solve complex multi-step research and data tasks.
Updated 15 days ago (2026-10-28 08:20 UTC)
1
MIT
deep-research-mcp-server
Search AI & Machine Learning RAG Systems
ssdeanx
A
license
Not graded
quality
D
maintenance
An MCP server that performs iterative deep research using Google Gemini 2.5 Flash with search grounding and URL context, producing professional Markdown reports.
Updated a year ago (2025-08-12 07:35 UTC)
8 npm
70
MIT

Appeared in Searches

A multifunctional research MCP server for a deep researcher subagent

Gemini Deep Research MCP

One-Click Install

Installation Methods

Using npx (Node.js)

Using uvx (Python)

Using pip

Antigravity

Prerequisites

Tools

1. start_deep_research

2. check_deep_research

Configuration

Development

License

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Langgraph Deep Search MCP

Gemini Research MCP Server

Gemini Agent MCP Server

deep-research-mcp-server

Appeared in Searches

1. `start_deep_research`

2. `check_deep_research`