gemini-research-mcp 0.1.1

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

Gemini Research MCP Server

The reference MCP server for AI-powered research using Gemini. Combines fast grounded search with comprehensive Deep Research capabilities, plus async patterns for maximum flexibility.

Tools

Tool	Description	Latency	API Used
research_web	Fast web search with citations	5-30 sec	Gemini 3 Flash + Google Search grounding
research_deep	Multi-step autonomous research	3-20 min	Deep Research Agent (Interactions API)
research_followup	Continue conversation after research	5-30 sec	Interactions API

Resources

Resource	Description
research://models	Lists available models & agents with capabilities

Workflow

research_web  ─── quick lookup ───▶  Got what you need?  ── yes ──▶ Done
       │                                        │
       │                                       no
       │                                        ▼
       └──────────────────────────────▶  research_deep  ──▶  Comprehensive report
                                       │         │
                               (auto-clarifies   │
                                vague queries)   │
                                                 ▼
                                        research_followup  ──▶  Dive deeper

Advanced Features

• Auto-Clarification (SEP-1330): research_deep automatically asks clarifying questions for vague queries via MCP Elicitation
• MCP Tasks (SEP-1732): Real-time progress with streaming updates
• File Search: Search your own data alongside web using file_search_store_names
• Follow-up: Continue conversations with previous_interaction_id
• Format Instructions: Control report structure (sections, tables, tone)
• Error Categorization: Typed error categories (AUTH_ERROR, RATE_LIMIT, SAFETY_BLOCK, etc.)
• Models Resource: Discover available models and agents via research://models

Installation

From PyPI

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

Environment Variables

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

Get your API key from https://aistudio.google.com/apikey

Environment File

Create .env from the template:

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

Usage

VS Code MCP (Recommended)

Add to .vscode/mcp.json:

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

Or use uvx (no install needed):

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

Command Line

# After uv sync
uv run gemini-research-mcp

# Or with uvx (no install needed)
uvx gemini-research-mcp

Architecture

Transport: stdio

The server uses stdio transport (VS Code spawns it as a child process):

mcp.run(transport="stdio")

Why stdio over HTTP?

Aspect	stdio	HTTP
VS Code integration	✅ Native	⚠️ Needs URL config
Latency	Microseconds	Milliseconds
Configuration	Zero	Complex (ports, SSL, auth)
Security	Process isolation	Network exposure risk
Concurrency	Single client	Multiple clients

For this use case (single developer, VS Code MCP), stdio is optimal. The 5-30 second Gemini API latency dominates; transport overhead is negligible.

MCP Tasks (SEP-1732)

research_deep uses MCP Tasks for background execution with real-time progress:

@mcp.tool(task=TaskConfig(mode="required"))
async def research_deep(..., progress: Progress):
    await progress.set_message("Researching...")
    await progress.increment(10)

Module Structure

src/gemini_research_mcp/
├── __init__.py     # Package exports (ErrorCategory, DeepResearchResult, etc.)
├── server.py       # MCP server (3 tools + 1 resource)
├── config.py       # Configuration management
├── types.py        # Data types, exceptions, ErrorCategory enum
├── quick.py        # research_web (grounded search)
├── deep.py         # research_deep + research_followup
├── clarifier.py    # Query analysis and clarification (internal)
├── citations.py    # Citation extraction and URL resolution
└── py.typed        # PEP 561 type marker

Development

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

Tests

# Unit tests only (no API calls)
uv run pytest

# With coverage report
uv run pytest --cov=src/gemini_research_mcp --cov-report=term-missing

# Include E2E tests (requires GEMINI_API_KEY)
uv run pytest -m e2e

Why "Gemini Research"?

This server provides two distinct research capabilities:

1. research_web - Uses Gemini 3 Flash + Google Search grounding (fast, ~10s)
2. research_deep - Uses Gemini Deep Research Agent (comprehensive, ~5-15min)

Thinking Configuration

Use thinking_level parameter:

• minimal - Minimize latency (chat/high-throughput)
• low - Balance speed and quality
• medium - Good reasoning depth
• high - Maximum reasoning (default, recommended for research)

The name "gemini-research-mcp" accurately reflects that both tools are Gemini-powered research capabilities.

Pricing Estimate

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.

Comparison

This server implements the most complete feature set among Gemini Deep Research MCP implementations:

Feature	gemini-research-mcp	Others
Tools	6	1-3
Sync deep research	✅	✅
Async start/poll pattern	✅	✅ (some)
Fast web search	✅	❌
Follow-up conversations	✅	❌
MCP Tasks (real-time progress)	✅	❌
MCP Features
Elicitation (SEP-1330)	✅	❌
Resources	✅	❌
Advanced
Format instructions	✅	⚠️ (some)
File search (RAG)	✅	❌
Typed error categories	✅	❌
Thinking level config	✅	⚠️ (some)
Citation extraction	✅	⚠️ (basic)

Key Differentiators

1. Complete workflow coverage: Web search → Clarify → Deep Research → Follow-up
2. MCP showcase: Implements SEP-1330 (Elicitation), SEP-1732 (Tasks), Resources
3. Two execution models: Blocking (MCP Tasks) + Non-blocking (start/check)
4. Production-ready: Error categorization, retry-aware exceptions, typed responses
5. First-class RAG: file_search_store_names for grounding on your data
6. Format control: Structure reports with format_instructions

License

MIT