Research Server

An MCP-based paper search chatbot that connects a Gemini client to local MCP tools for arXiv search and paper metadata lookup.

What's Included

• mcp_chatbot.py: interactive chatbot that connects to configured MCP servers and routes tool calls through Gemini
• research_server.py: MCP server that searches arXiv and stores paper metadata locally
• papers/: generated cache of paper search results, grouped by topic
• server_config.json: MCP server launch configuration used by the chatbot

Requirements

• Python 3.14 or newer
• uv installed locally
• A Google Gemini API key

Quick Start

1. Clone the repo and enter the project directory.
2. Create a local .env file in the project root and add your Google API key.
3. Install dependencies with uv sync.
4. Start the research server.
5. Start the chatbot in a second terminal and ask a question.

git clone [email protected]:akhileshvj/MCPChatbotForPapers.git
cd MCPChatbotForPapers
uv sync
uv run python research_server.py
uv run python mcp_chatbot.py

Initialize the Project

Clone the repository and move into it:

git clone [email protected]:akhileshvj/MCPChatbotForPapers.git
cd MCPChatbotForPapers

Create and use the virtual environment managed by uv:

uv sync

If you prefer to install from the pinned requirements file instead of pyproject.toml, use:

uv pip install -r requirements.txt

Configure Environment Variables

Create a .env file in the project root and add your Gemini API key:

GOOGLE_API_KEY=your_google_genai_api_key

Keep this file local. It is not meant to be pushed to GitHub.

The chatbot loads environment variables with python-dotenv.

Install Dependencies

If you are starting from a clean environment, install the project dependencies with:

uv sync

That will install the packages listed in pyproject.toml, including:

• google-genai
• mcp[cli]
• python-dotenv
• arxiv
• fastapi
• uvicorn

Run the MCP Research Server

The research server exposes the paper search tools over MCP stdio:

uv run python research_server.py

Run the Chatbot

Start the interactive chatbot in a second terminal:

uv run python mcp_chatbot.py

The chatbot reads server_config.json, launches the configured MCP servers, and then waits for queries at the prompt.

Example Usage

Inside the chatbot, try prompts like:

Search papers about diffusion models
Find recent papers on quantum computing
Look up paper details for a saved paper ID

How It Works

1. mcp_chatbot.py connects to the MCP servers listed in server_config.json.
2. Gemini receives the available tool schemas.
3. When Gemini requests a tool call, the chatbot routes it to the correct MCP server.
4. research_server.py searches arXiv and stores results under papers/<topic>/papers_info.json.

Notes

• papers/ is populated automatically when you run searches.
• If you change server commands in server_config.json, restart the chatbot so it reloads the config.
• The project currently uses local stdio-based MCP servers, so each server process must be runnable from the repository root.

Troubleshooting

• If the chatbot cannot connect to Gemini, check that GOOGLE_API_KEY is set in .env.
• If research_server.py fails to start, make sure arxiv and mcp are installed in the active environment.
• If you see stale results, delete the relevant folder under papers/ and run the search again.