MCP Integration

This is a ** sampleproject** demonstrating how to connect an OpenAI Agent or other LLMs (with tool-calling capability) to an MCP server through langchain or openai-agent framework for enhanced paper search and chat functionality.
It includes:

• Creating and running an MCP server
• Connecting an LLM (local or remote) to the MCP server
• Running an interactive chatbot that can search arXiv papers and store results

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Usage

Below are usage examples for each file script.

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tool_function.py

It use arxiv lib to search and extract info from query of user. There are two function calling search and extract information.

Run the test tool function

poetry run python tool_function.py

research_server.py

An example of creating a local MCP server with stdio connection.

Run the test tool function

npx @modelcontextprotocol/inspector run research_server.py

Serving Locally with vLLMs

You can run this project with a local vLLM for faster inference.
For GPT-OSS-20B, see GPT-OSS instructions.

Important: The LLM you choose must have tool-calling capability to work with the MCP server.

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Start a Local vLLM Server

Open new terminal then run.

vllm serve openai/gpt-oss-20b

Then update init_agent() to point to your vLLM endpoint (default: http://localhost:8000/v1).

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chatbot_mcp.py

python chatbot_mcp.py

Initializes an agent and connects to the MCP server. Each agent maintains its own session.