Deep Research

by hajime-y

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About

Provides web search and advanced research capabilities with specialized tools for browsing, document analysis, media processing, and archive searching to gather information from diverse sources.

Details

Author
hajime-y
Repository
Hajime-Y/deep-research-mcp
GitHub stars
12
License
Apache License 2.0
Categories
Search, Knowledge Base, Other, Developer Tools, Design, Workplace, File Management, AI, Media, Infrastructure
Tags
#web

- Web search and information gathering
- PDF and document analysis
- Image analysis and description
- YouTube transcript retrieval
- Archive site search

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Deep Research
    Command (node, npx, python, etc.) docker
    Arguments
    • Argument 1 run
    • Argument 2 -i
    • Argument 3 --rm
    • Argument 4 -e
    • Argument 5 OPENAI_API_KEY=your_openai_api_key
    • Argument 6 -e
    • Argument 7 HF_TOKEN=your_huggingface_token
    • Argument 8 -e
    • Argument 9 SERPER_API_KEY=your_serper_api_key
    • Argument 10 deep-research-mcp
    Environment
    • HF_TOKEN your_huggingface_token
    • OPENAI_API_KEY your_openai_api_key
    • SERPER_API_KEY your_serper_api_key

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Start the MCP server:

uv run deep_research.py

This will launch the deep_research agent as an MCP server.

Create a .env file in the root directory of the project and set the following environment variables:

OPENAI_API_KEY=your_openai_api_key
HF_TOKEN=your_huggingface_token
SERPER_API_KEY=your_serper_api_key

You can obtain a SERPER_API_KEY by signing up at Serper.dev.

You can also run this MCP server in a Docker container:

```bash

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "deep research": {
            "env": {
                "HF_TOKEN": "your_huggingface_token",
                "OPENAI_API_KEY": "your_openai_api_key",
                "SERPER_API_KEY": "your_serper_api_key"
            },
            "args": [
                "run",
                "-i",
                "--rm",
                "-e",
                "OPENAI_API_KEY=your_openai_api_key",
                "-e",
                "HF_TOKEN=your_huggingface_token",
                "-e",
                "SERPER_API_KEY=your_serper_api_key",
                "deep-research-mcp"
            ],
            "command": "docker"
        }
    }
}

Linux

{
    "env": {
        "HF_TOKEN": "your_huggingface_token",
        "OPENAI_API_KEY": "your_openai_api_key",
        "SERPER_API_KEY": "your_serper_api_key"
    },
    "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAI_API_KEY=your_openai_api_key",
        "-e",
        "HF_TOKEN=your_huggingface_token",
        "-e",
        "SERPER_API_KEY=your_serper_api_key",
        "deep-research-mcp"
    ],
    "command": "docker"
}

Macos

{
    "env": {
        "HF_TOKEN": "your_huggingface_token",
        "OPENAI_API_KEY": "your_openai_api_key",
        "SERPER_API_KEY": "your_serper_api_key"
    },
    "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAI_API_KEY=your_openai_api_key",
        "-e",
        "HF_TOKEN=your_huggingface_token",
        "-e",
        "SERPER_API_KEY=your_serper_api_key",
        "deep-research-mcp"
    ],
    "command": "docker"
}

Windows

{
    "env": {
        "HF_TOKEN": "your_huggingface_token",
        "OPENAI_API_KEY": "your_openai_api_key",
        "SERPER_API_KEY": "your_serper_api_key"
    },
    "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAI_API_KEY=your_openai_api_key",
        "-e",
        "HF_TOKEN=your_huggingface_token",
        "-e",
        "SERPER_API_KEY=your_serper_api_key",
        "deep-research-mcp"
    ],
    "command": "docker"
}
Deep Research is an agent-based tool that provides web search and advanced research capabilities. It leverages HuggingFace's`smolagents`and is implemented as an MCP server. This project is based on[HuggingFace's open_deep_research example. - Web search and information gathering - PDF and document analysis - Image analysis and description - YouTube transcript retrieval - Archive site search - Python 3.11 or higher - `uv`package manager - The following API keys: - OpenAI API key - HuggingFace token - SerpAPI key ``` `git clone https://github.com/Hajime-Y/deep-research-mcp.git cd deep-research-mcp` ``` - Create a virtual environment and install dependencies: ``` `uv venv source .venv/bin/activate # For Linux or Mac # .venv\Scripts\activate # For Windows uv sync` ``` Create a`.env`file in the root directory of the project and set the following environment variables: ``` `OPENAI_API_KEY=your_openai_api_key HF_TOKEN=your_huggingface_token SERPER_API_KEY=your_serper_api_key` ``` You can obtain a SERPER_API_KEY by signing up at](https://github.com/huggingface/smolagents/tree/main/examples/open_deep_research)[Serper.dev. This will launch the`deep_research`agent as an MCP server. You can also run this MCP server in a Docker container: ``` `# Build the Docker image docker build -t deep-research-mcp . # Run with required API keys docker run -p 8080:8080 \ -e OPENAI_API_KEY=your_openai_api_key \ -e HF_TOKEN=your_huggingface_token \ -e SERPER_API_KEY=your_serper_api_key \ deep-research-mcp` ``` To register this Docker container as an MCP server in different clients: Add the following to your Claude Desktop configuration file (typically located at`~/.config/Claude/claude_desktop_config.json`on Linux,`~/Library/Application Support/Claude/claude_desktop_config.json`on macOS, or`%APPDATA%\Claude\claude_desktop_config.json`on Windows): ``` `{ "mcpServers": { "deep-research-mcp": { "command": "docker", "args": ](https://serper.dev/signup)[ "run", "-i", "--rm", "-e", "OPENAI_API_KEY=your_openai_api_key", "-e", "HF_TOKEN=your_huggingface_token", "-e", "SERPER_API_KEY=your_serper_api_key", "deep-research-mcp" ] } } }` ``` For Cursor IDE, add the following configuration: ``` `{ "mcpServers": { "deep-research-mcp": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "OPENAI_API_KEY=your_openai_api_key", "-e", "HF_TOKEN=your_huggingface_token", "-e", "SERPER_API_KEY=your_serper_api_key", "deep-research-mcp" ] } } }` ``` If you're running the MCP server on a remote machine or exposing it as a service, you can use the URL-based configuration: ``` `{ "mcpServers": { "deep-research-mcp": { "url": "http://your-server-address:8080/mcp", "type": "sse" } } }` ``` - `deep_research.py`: Entry point for the MCP server - `create_agent.py`: Agent creation and configuration - `scripts/`: Various tools and utilities - `text_web_browser.py`: Text-based web browser - `text_inspector_tool.py`: File inspection tool - `visual_qa.py`: Image analysis tool - `mdconvert.py`: Converts various file formats to Markdown This project is provided under the Apache License 2.0. This project uses code from HuggingFace's`smolagents`and Microsoft's`autogen`projects. Search global news using natural language. Webz.io News Search API returns the most relevant articles and content, with filters for source, country, language, date, sentiment, and category. BYOK Reddit/YouTube research CLI with a tamper-evident audit log and an MCP server. An MCP server providing search capabilities for Reddit, YouTube, and Twitter. Provides search capabilities and data retrieval from SerpAPI and YouTube for AI assistants. Get YouTube transcripts, search videos, browse channels, and extract playlists from any AI agent — powered by TranscriptAPI.com with no API key required. Search YouTube videos and retrieve their transcripts using the YouTube API. Connect AI assistants to YouTube - search, transcripts, metadata, and more. Self-hosted YouTube research MCP with 17 tools for search, transcripts, timestamped frames, comments, and private local semantic corpora. Search a YouTube video's transcript and read its frames — every answer cites a clickable timestamp. A set of tools to interact with YouTube, including video search, transcript extraction, and comment retrieval. Finds background music for YouTube shorts by analyzing script content and recommending tracks from YouTube Music.
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