gemini-research-mcp 0.1.3

MCP server for AI-powered research using Gemini: quick grounded search + Deep Research Agent

Gemini Research MCP Server

MCP server for AI-powered research using Gemini. Fast grounded search + comprehensive Deep Research.

Tools

Tool	Description	Latency
research_web	Fast web search with citations	5-30 sec
research_deep	Multi-step autonomous research	3-20 min
research_followup	Continue conversation after research	5-30 sec

Workflow

research_web  ─── quick lookup ───▶  Got what you need?  ── yes ──▶ Done
       │                                        │
       │                                       no
       │                                        ▼
       └──────────────────────────────▶  research_deep  ──▶  Comprehensive report
                                                 │
                                                 ▼
                                        research_followup  ──▶  Dive deeper

Features

• Auto-Clarification: research_deep asks clarifying questions for vague queries via MCP Elicitation
• MCP Tasks: Real-time progress with streaming updates
• File Search: Search your own data alongside web using file_search_store_names
• Format Instructions: Control report structure (sections, tables, tone)
• Models Resource: Discover available models via research://models

Installation

pip install gemini-research-mcp
# or
uv add gemini-research-mcp

From source:

git clone https://github.com/fortaine/gemini-research-mcp
cd gemini-research-mcp
uv sync

Configuration

Variable	Required	Default	Description
GEMINI_API_KEY	Yes	—	Google AI Studio API key
GEMINI_MODEL	No	gemini-3-flash-preview	Model for research_web
DEEP_RESEARCH_AGENT	No	deep-research-pro-preview-12-2025	Agent for research_deep

cp .env.example .env
# Edit .env with your API key

Usage

VS Code MCP

Add to .vscode/mcp.json:

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

Or run from source:

{
  "servers": {
    "gemini-research": {
      "command": "uv",
      "args": ["--directory", "path/to/gemini-research-mcp", "run", "gemini-research-mcp"],
      "envFile": "${workspaceFolder}/path/to/gemini-research-mcp/.env"
    }
  }
}

Command Line

uv run gemini-research-mcp
# or
uvx gemini-research-mcp

Development

uv sync --extra dev
uv run pytest
uv run mypy src/
uv run ruff check src/

Tests

uv run pytest                    # Unit tests
uv run pytest -m e2e             # E2E tests (requires GEMINI_API_KEY)
uv run pytest --cov=src/gemini_research_mcp  # With coverage

Pricing

Tool	Typical Cost
research_web	~$0.01-0.05 per query
research_deep	~$2-5 per task

Deep Research uses ~80-160 searches and ~250k-900k tokens per task.

License

MIT