Deep Research MCP Server: AI-Driven Insights & Automation

Deep Research MCP Server 🚀

📚 Table of Contents

• ✨ How It Works
• 🌟 Features
• ⚙️ Requirements
• 🛠️ Setup
• 🚀 Usage
• 📜 License

🤖 Deep Research Gemini

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and Gemini large language models. Available as a Model Context Protocol (MCP) tool for seamless integration with AI agents.

The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction over time and deep dive into a topic. Goal is to keep the repo size at <500 LoC so it is easy to understand and build on top of.

🔄 Research Agent + PostgreSQL Integration

The research agent works seamlessly with PostgreSQL to create an efficient research system:

1. Knowledge Persistence : Each research finding and URL is stored in PostgreSQL, creating a growing knowledge base
2. Smart Caching : Previously processed URLs are tracked to avoid duplicate processing
3. Learning Context : The agent can reference past findings to guide new research directions
4. Query Optimization : Similar research queries can leverage existing database knowledge
5. Efficient Retrieval : Fast access to historical research data through indexed PostgreSQL queries

This integration enables the agent to build upon previous research while maintaining a lightweight codebase.

✨ How It Works

flowchart TB
    subgraph Input
        Q[User Query]
        B[Breadth Parameter]
        D[Depth Parameter]
    end

    DR[Deep Research] -->
    SQ[SERP Queries] -->
    PR[Process Results]

    subgraph Results[Results]
        direction TB
        NL((Learnings))
        ND((Directions))
    end

    PR --> NL
    PR --> ND

    DP{depth > 0?}

    RD["Next Direction:
    - Prior Goals
    - New Questions
    - Learnings"]

    MR[Markdown Report]
    DB[PostgreSQL Database]

    %% Main Flow
    Q & B & D --> DR

    %% Results to Decision
    NL & ND --> DP

    %% Circular Flow
    DP -->|Yes| RD
    RD -->|New Context| DR

    %% Final Output
    DP -->|No| MR
    DR --> DB
    DB --> NL

    %% Styling
    classDef input fill:#7bed9f,stroke:#2ed573,color:black
    classDef process fill:#70a1ff,stroke:#1e90ff,color:black
    classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
    classDef output fill:#ff4757,stroke:#ff6b81,color:black
    classDef results fill:#a8e6cf,stroke:#3b7a57,color:black

    class Q,B,D input
    class DR,SQ,PR process
    class DP,RD recursive
    class MR output
    class NL,ND results
    class DB output

🌟 Features

• MCP Integration : Available as a Model Context Protocol tool for seamless integration with AI agents
• Iterative Research : Performs deep research by iteratively generating search queries, processing results, and diving deeper based on findings
• Intelligent Query Generation : Uses Gemini LLMs to generate targeted search queries based on research goals and previous findings
• Depth & Breadth Control: Configurable parameters to control how wide (breadth) and deep (depth) the research goes
• Smart Follow-up : Generates follow-up questions to better understand research needs
• Comprehensive Reports : Produces detailed markdown reports with findings and sources
• Concurrent Processing : Handles multiple searches and result processing in parallel for efficiency
• Persistent Knowledge with PostgreSQL : 🐘 Leverages a PostgreSQL database for storing research data, ensuring data persistence across sessions and enabling efficient retrieval of past findings. This allows the agent to build upon previous knowledge and avoid redundant research.

⚙️ Requirements

• Node.js environment (v22.x recommended)
• API keys for:
  • Firecrawl API 🕸️ (for web search and content extraction)
  • Gemini API 🧠 (for Gemini 2.0 models)
• PostgreSQL database 🐘 (running locally or remotely)

🛠️ Setup

Node.js

1. Clone the repository
2. Install dependencies:

npm install

• Set up environment variables in a .env.local file:

  GEMINI_API_KEY="your_gemini_key"
  FIRECRAWL_KEY="your_firecrawl_key"

  Optional: If you want to use your self-hosted Firecrawl

  FIRECRAWL_BASE_URL="http://localhost:3002"

  DATABASE_URL="postgresql://username:password@localhost:5432/db" # 🐘 PostgreSQL connection string

• Build the project:

  npm run build

🐘 PostgreSQL Database for Persistent Research

This project uses PostgreSQL to store research data, providing local storage for learnings and visited URLs. This allows the agent to:

• Recall previous research: Avoid re-running the same queries and re-processing the same content.
• Build upon existing knowledge: Use past learnings to guide future research directions.
• Maintain a consistent knowledge base: Ensure that the agent's knowledge is consistent across sessions.

1. Ensure you have a PostgreSQL database running locally or remotely.
2. Set the DATABASE_URL environment variable in your .env.local file with the connection string to your PostgreSQL database.
   * The DATABASE_URL should follow the format: postgresql://user:password@host:port/database
   * Example: postgresql://user:password@host:port/database
3. The database will be automatically created and the research_data table will be created if it doesn't exist.

Testing the Database Connection

To test the database connection, run the following command:

node src/db.ts

This will attempt to connect to the database and print a success or failure message to the console.

🚀 Usage

As an MCP Tool

The deep research functionality is available as an MCP tool that can be used by AI agents. To start the MCP server:

node --env-file .env.local dist/mcp-server.js

The tool provides the following parameters:

• query (string): The research query to investigate
• depth (number, 1-5): How deep to go in the research tree
• breadth (number, 1-5): How broad to make each research level
• existingLearnings (string[], optional): Array of existing research findings to build upon

Example tool usage in an agent:

const result = await mcp.invoke("deep-research", {
  query: "What are the latest developments in quantum computing?",
  depth: 3,
  breadth: 3
});

The tool returns:

• A detailed markdown report of the findings
• List of sources used in the research
• Metadata about learnings and visited URLs

Standalone Usage

For standalone usage without MCP, you can use the CLI interface:

npm run start "Your research query here"

To test the MCP server with the inspector:

npx @modelcontextprotocol/inspector node --env-file .env.local dist/mcp-server.js

🔗 Technologies Used

• Node.js 🟢
• TypeScript 🟦
• Gemini API 🧠
• Firecrawl API 🕸️
• PostgreSQL 🐘
• Model Context Protocol 🧩

📜 License

MIT License - feel free to use and modify as needed.