Which integrations are available for this server?

Integrates with the Google Search API to perform multi-step web research, enabling the agent to conduct iterative searches and verify information with grounding metadata. Utilizes various Gemini models (including Flash and Pro versions) for query generation, content analysis, research reflection, and the synthesis of structured reports with proper citations. Employs a LangGraph-powered workflow to manage sophisticated multi-step research agents, including automated reflection and gap analysis loops.

How do I use Deep Research Agent MCP Server?

1. Click on "Install 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., "@Deep Research Agent MCP Server Research the impact of generative AI on software engineering workflows with citations." 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.

Deep Research Agent MCP Server

🔍 Intelligent AI Research Agent - A sophisticated LangGraph-powered research agent wrapped as a Model Context Protocol (MCP) server for seamless integration with AI assistants like Claude, Cursor, and other MCP-compatible clients.

✨ Features

Advanced Research Capabilities

Multi-Step Research: Conducts iterative web research with reflection and refinement loops
Google Search Integration: Uses Google Search API with advanced grounding metadata
AI-Powered Analysis: Leverages multiple Gemini models (2.0 Flash, 2.5 Flash, 2.5 Pro) for different tasks
Comprehensive Reports: Generates structured research reports with proper citations and source verification
Configurable Depth: Customizable research loops and query generation parameters

MCP Server Integration

FastMCP Server: Built on FastMCP for seamless MCP protocol support
Real-time Streaming: Progress updates streamed to clients during research execution
HTTP Transport: Accessible via HTTP for remote deployment and integration
Health Monitoring: Built-in health checks and statistics endpoints
Error Handling: Robust error handling with detailed logging

Deployment Ready

Docker Support: Containerized for easy deployment
Render Integration: One-click deployment to Render platform
Environment Configuration: Flexible configuration via environment variables
Scalable Architecture: Designed for concurrent research requests

Architecture

Research Agent Workflow

graph TD
    A[Research Topic Input] --> B[Query Generation]
    B --> C[Web Research]
    C --> D[Content Analysis]
    D --> E[Reflection & Gap Analysis]
    E --> F{Research Complete?}
    F -->|No| G[Generate Follow-up Queries]
    G --> C
    F -->|Yes| H[Final Report Generation]
    H --> I[Structured Output with Citations]
    
    subgraph "AI Models Used"
        J[Gemini 2.0 Flash<br/>Query Generation]
        K[Gemini 2.0 Flash<br/>Web Research]
        L[Gemini 2.5 Flash<br/>Reflection]
        M[Gemini 2.5 Pro<br/>Final Report]
    end
    
    B -.-> J
    C -.-> K
    E -.-> L
    H -.-> M

MCP Server Architecture

graph TB
    subgraph "Client Applications"
        A1[Claude Desktop]
        A2[Cursor IDE]
        A3[Custom MCP Client]
    end
    
    subgraph "MCP Server (FastMCP)"
        B1[HTTP Transport Layer]
        B2[Research Tool Handler]
        B3[Progress Streaming]
        B4[Health & Stats Endpoints]
    end
    
    subgraph "LangGraph Research Agent"
        C1[Query Generation Node]
        C2[Web Research Node]
        C3[Reflection Node]
        C4[Final Answer Node]
    end
    
    subgraph "External Services"
        D1[Google Search API]
        D2[Gemini AI Models]
    end
    
    A1 --> B1
    A2 --> B1
    A3 --> B1
    B1 --> B2
    B2 --> B3
    B2 --> C1
    C1 --> C2
    C2 --> C3
    C3 --> C4
    C2 --> D1
    C1 --> D2
    C3 --> D2
    C4 --> D2

Deployment Architecture

graph TB
    subgraph "Development"
        A1[Local Development]
        A2[Docker Compose]
    end
    
    subgraph "Production Deployment"
        B1[Render Platform]
        B2[Docker Container]
        B3[Custom Cloud Deploy]
    end
    
    subgraph "MCP Server Container"
        C1[FastMCP HTTP Server]
        C2[LangGraph Agent]
        C3[Health Monitoring]
        C4[Environment Config]
    end
    
    A1 --> C1
    A2 --> C1
    B1 --> C1
    B2 --> C1
    B3 --> C1

🚀 Quick Start

1. Render Deployment (Recommended)

Deploy to Render in 5 minutes:

Fork this repository to your GitHub account
Create Render account at render.com

Deploy service:

Click "New +" → "Web Service"
Connect your GitHub repository

Configure settings:

Name: deep-research-mcp-server
Runtime: Python 3
Build Command: pip install -r requirements.txt
Start Command: python -m src.mcp_server.server

Add environment variables:

GEMINI_API_KEY = your_gemini_api_key_here
PORT = 8000

Deploy and get your server URL: https://your-service-name.onrender.com

2. Local Development

# Clone repository
git clone https://github.com/your-username/deep-research-mcp.git
cd deep-research-mcp

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export GEMINI_API_KEY=your_gemini_api_key_here

# Run MCP server
python -m src.mcp_server.server

3. Docker Deployment

# Build Docker image
docker build -t deep-research-mcp .

# Run container
docker run -p 8000:8000 \
  -e GEMINI_API_KEY=your_gemini_api_key \
  deep-research-mcp

🔧 Configuration

Environment Variables

Variable	Description	Default	Required
`GEMINI_API_KEY`	Google Gemini API key	-	✅
`PORT`	Server port	8000	❌
`HOST`	Server host	0.0.0.0	❌
`LOG_LEVEL`	Logging level	info	❌

Research Parameters

Configure research behavior through the MCP tool parameters:

{
  "topic": "Your research question",
  "max_research_loops": 2,
  "initial_search_query_count": 3,
  "reasoning_model": "gemini-2.5-pro"
}

📖 Usage

With Claude Desktop

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "deep-research": {
      "url": "https://your-service-name.onrender.com/mcp/"
    }
  }
}

With Cursor IDE

Add to Cursor settings → MCP Servers:

{
  "mcpServers": {
    "deep-research": {
      "url": "https://your-service-name.onrender.com/mcp/"
    }
  }
}

Python Client Example

from fastmcp import Client
import asyncio

async def research_example():
    client = Client("http://localhost:8000/mcp/")
    async with client:
        result = await client.call_tool("research", {
            "topic": "Latest developments in quantum computing",
            "max_research_loops": 3,
            "initial_search_query_count": 4
        })
        
        print("Research Report:")
        print(result["report"])
        print(f"\nSources: {len(result['sources'])}")
        print(f"Execution time: {result['metadata']['execution_time']:.2f}s")

asyncio.run(research_example())

🛠️ Development

Project Structure

deep-research-mcp/
├── src/
│   ├── agent/                    # LangGraph research agent
│   │   ├── app.py               # FastAPI app
│   │   ├── graph.py             # LangGraph workflow definition
│   │   ├── state.py             # State management
│   │   ├── prompts.py           # AI prompts
│   │   ├── tools_and_schemas.py # Tools and data schemas
│   │   ├── configuration.py     # Agent configuration
│   │   └── utils.py             # Utility functions
│   └── mcp_server/              # MCP server implementation
│       ├── server.py            # FastMCP server
│       ├── agent_adapter.py     # Agent wrapper
│       ├── config.py            # Configuration management
│       └── utils.py             # Server utilities
├── ClinicalTrials-MCP-Server/   # Additional MCP server example
├── examples/                    # Usage examples
├── requirements.txt             # Python dependencies
├── pyproject.toml              # Project configuration
├── render.yaml                 # Render deployment config
└── README.md                   # This file

Local Testing

# Install development dependencies
pip install -r requirements.txt

# Run tests
python -m pytest tests/

# Start server in development mode
python -m src.mcp_server.server

# Test health endpoint
curl http://localhost:8000/health

# Test MCP endpoint
curl -X POST http://localhost:8000/mcp/ \
  -H "Content-Type: application/json" \
  -d '{"method": "tools/list", "params": {}}'

📊 Monitoring

Health Check

curl https://your-service-name.onrender.com/health

Response:

{
  "status": "healthy",
  "service": "Deep Research MCP Server",
  "version": "1.0.0",
  "agent_status": "healthy"
}

Statistics

curl https://your-service-name.onrender.com/stats

Logging

The server provides structured logging with:

Request/response tracking
Research progress updates
Error reporting and debugging
Performance metrics

🔒 Security

API Key Protection: Environment variable-based secret management
Input Validation: Comprehensive input sanitization
Rate Limiting: Built-in request throttling
Error Handling: Secure error responses without sensitive data exposure

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Deep Research Agent MCP Server

Deep Research Agent MCP Server

✨ Features

Advanced Research Capabilities

MCP Server Integration

Deployment Ready

Architecture

Research Agent Workflow

MCP Server Architecture

Deployment Architecture

🚀 Quick Start

1. Render Deployment (Recommended)

2. Local Development

3. Docker Deployment

🔧 Configuration

Environment Variables

Research Parameters

📖 Usage

With Claude Desktop

With Cursor IDE

Python Client Example

🛠️ Development

Project Structure

Local Testing

📊 Monitoring

Health Check

Statistics

Logging

🔒 Security

📝 License

Resources

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