🔬 Research Paper MCP Server

A powerful Model Context Protocol (MCP) server for AI-assisted academic research, hosted on Hugging Face Spaces. Enables LLMs to search the web, read webpages, and discover research papers with citations and PDF links — all through a clean SSE interface.

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🌐 Live Endpoints

Service	URL
MCP Server (SSE)	https://Codemaster67-ResearchPaperMCP.hf.space/sse
LangChain Agent API	https://Codemaster67-GoolgeLangchainAgent.hf.space/

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📖 What Is This?

This project is split into two independent services that work together:

• MCP_SERVER/ — A FastMCP server that exposes research tools as MCP-compatible endpoints, hosted on Hugging Face Spaces via SSE.
• langchain_backend/ — A FastAPI service that wraps the MCP server with a LangGraph ReAct agent (powered by Google Gemini), and exposes a REST chat API consumed by a frontend.

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📁 Project Structure

MCP_For_Researcher/
│
├── MCP_SERVER/                   # 🧠 MCP Tool Server (Hugging Face Spaces)
│   ├── mcp_server.py             #   FastMCP server — defines all 5 MCP tools
│   ├── Testmcp.py                #   Quick local test script for MCP tools
│   └── dockerfile                #   Docker config for HF Spaces deployment
│
├── langchain_backend/            # 🤖 LangChain Agent + FastAPI Backend
│   ├── agent.py                  #   FastAPI app — LangGraph ReAct agent + /initialize & /chat endpoints
│   └── TestUI.py                 #   Streamlit test UI for the agent backend
│
└── README.md

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🧠 Part 1 — MCP Server (MCP_SERVER/)

The MCP server exposes 5 research tools that any MCP-compatible AI agent can call directly over SSE.

Available Tools

Tool	Description
🔍 search_web	Google search via SerpAPI — returns titles, links, and snippets
📄 fetch_web_content	Extracts full Markdown content from any URL using Jina Reader
📚 academic_research	Queries Semantic Scholar (with automatic OpenAlex fallback)
🔗 get_paper_id	Resolves a paper title to its DOI, ArXiv ID, and OpenAlex ID
🧩 find_related_papers	Finds similar papers by Semantic Scholar / OpenAlex / DOI

Tool Reference

search_web(query, required_links)

General web + YouTube search using Google (via SerpAPI).

search_web(query="attention is all you need explained", required_links=5)
# Returns: [{ title, link, snippet }, ...]

fetch_web_content(url)

Reads and extracts full Markdown text from any webpage. Powered by Jina Reader. (No YouTube support.)

fetch_web_content(url="https://arxiv.org/abs/1706.03762")
# Returns: Full page text as Markdown

academic_research(query, limit)

Searches academic databases. Tries Semantic Scholar first; falls back to OpenAlex automatically.

academic_research(query="transformer models NLP", limit=5)
# Returns: [{ title, authors, year, citationCount, abstract, openAccessPdf, externalIds }, ...]

get_paper_id(query)

Resolves a paper title/keywords to all known identifiers.

get_paper_id(query="BERT pre-training deep bidirectional transformers")
# Returns: { title, paperId, doi, openalex, arxiv, source }

find_related_papers(paper_id, limit)

Finds recommended/similar papers. Accepts Semantic Scholar ID, OpenAlex ID, or DOI.

find_related_papers(paper_id="204e3073870fae3d05bcbc2f6a8e263d9b72e776", limit=5)
# Returns: [{ title, authors, year, citationCount, url }, ...]

Running the MCP Server Locally

cd MCP_SERVER

# Install dependencies
pip install fastmcp requests

# Run the server (SSE on port 7860)
python mcp_server.py

Docker Deployment (HF Spaces)

cd MCP_SERVER
docker build -t research-mcp .
docker run -p 7860:7860 research-mcp

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🤖 Part 2 — LangChain Agent Backend (langchain_backend/)

A FastAPI service that acts as the bridge between your frontend and the MCP server. On startup it connects to the live MCP SSE endpoint and fetches all available tools. Users bring their own Gemini API key.

API Endpoints

Method	Endpoint	Description
POST	/initialize	Initialize the ReAct agent with your Gemini API key + model
POST	/chat	Send a message (+ optional file) and receive an AI response

Running the Agent Backend Locally

cd langchain_backend

# Install dependencies
pip install fastapi uvicorn langchain-mcp-adapters langchain-google-genai langgraph

# Start the server
python agent.py
# API available at http://localhost:7860

Initialize the Agent

curl -X POST http://localhost:7860/initialize \
  -F "api_key=YOUR_GOOGLE_GEMINI_API_KEY" \
  -F "model_name=gemini-2.5-flash"

Chat with the Agent

curl -X POST http://localhost:7860/chat \
  -F "message=Find me the top 5 papers on vision transformers with citation counts"

You can also attach a file (PDF, image, or text) to the /chat endpoint as multipart form data.

Testing with the Streamlit UI

TestUI.py provides a quick browser-based interface to test the agent backend without a production frontend:

cd langchain_backend
pip install streamlit
streamlit run TestUI.py

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🔌 Connecting the MCP Server to Other Clients

The MCP SSE endpoint can be used directly by any MCP-compatible client — no LangChain required.

LangChain / LangGraph

from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient({
    "ResearchAgent": {
        "url": "https://Codemaster67-ResearchPaperMCP.hf.space/sse",
        "transport": "sse"
    }
})
tools = await client.get_tools()

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "ResearchAgent": {
      "url": "https://Codemaster67-ResearchPaperMCP.hf.space/sse",
      "transport": "sse"
    }
  }
}

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📦 Tech Stack

Component	Technology
MCP Framework	FastMCP
Web Search	SerpAPI (Google engine)
Web Reader	Jina Reader (r.jina.ai)
Academic Search	Semantic Scholar API + OpenAlex
Agent Framework	LangGraph ReAct Agent
LLM	Google Gemini (via langchain-google-genai)
Agent Backend	FastAPI + Uvicorn
Test UI	Streamlit
Hosting	Hugging Face Spaces
Containerization	Docker (Python 3.10-slim)

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🔑 API Keys

MCP Server (MCP_SERVER/mcp_server.py)

These keys are baked into the server. For production, move them to HF Spaces Secrets or environment variables.

Variable	Service	Purpose
SERP_API_KEY	SerpAPI	Google web search
JINA_API_KEY	Jina AI	Webpage content extraction
OPEN_ALEX_API_KEY	OpenAlex	Fallback academic search polite pool

LangChain Backend (langchain_backend/agent.py)

The Gemini API key is provided at runtime by the user via /initialize — it is never stored server-side.

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🙌 Contributing

Pull requests are welcome! Feel free to open an issue for bugs, feature requests, or new tool ideas.

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