Research MCP Server

A local, rule-based research assistant for searching and analyzing academic papers from arXiv. No API keys, no LLM calls — just Python standard libraries, the arxiv package, FastMCP for the MCP server, and keyword-frequency heuristics.

Available as both a CLI tool and a Model Context Protocol (MCP) server for Cursor, Claude Desktop, and other MCP clients.

Features

Related MCP server: paper-mcp

Project Structure

mcp_server_paper/
├── research.py       # Core engine (search, rank, summarize, keywords, citations)
├── main.py           # CLI entry point
├── mcp_server.py     # MCP server (stdio transport)
├── requirements.txt  # Python dependencies
├── tests/
│   ├── test_research.py      # Unit tests (mocked, no network)
│   └── test_integration.py   # CLI + MCP tool integration tests
└── README.md

Requirements

• Python 3.10+

• Internet connection (for arXiv fetch only)

• Dependencies: arxiv, fastmcp, pytest (see requirements.txt)

Installation

git clone <your-repo-url>
cd mcp_server_paper
pip install -r requirements.txt

CLI Usage

Search arXiv and print formatted results:

python main.py --query "AI agents"

Options:

Example:

python main.py --query "transformer attention" --max-results 3

Example Output

🔍 Query: AI agents

📊 Total papers found: 5

━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📄 Paper 1: A cybersecurity AI agent selection and decision support framework

🧠 Summary:
This paper presents a novel, structured decision support framework...

🔑 Keywords:
framework, cybersecurity, learning, standards, industry

📚 Citation:
Masike Malatji (2025). A cybersecurity AI agent selection and decision support framework.

🔗 Link:
http://arxiv.org/abs/2510.01751v1

━━━━━━━━━━━━━━━━━━━━━━━━━━━━

MCP Server Usage

The MCP server exposes paper search and analysis as tools over stdio transport, compatible with Cursor and Claude Desktop.

Start the server manually

python mcp_server.py

The server reads JSON-RPC from stdin and writes responses to stdout. Do not print debug output to stdout when running in MCP mode — logs go to stderr.

Configure in Cursor

Add to your Cursor MCP settings (Settings → MCP → Add new global MCP server or edit ~/.cursor/mcp.json):

{
  "mcpServers": {
    "research-papers": {
      "command": "python",
      "args": ["C:/mcp_server_paper/mcp_server.py"]
    }
  }
}

Use the absolute path to mcp_server.py on your machine.

Configure in Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "research-papers": {
      "command": "python",
      "args": ["/absolute/path/to/mcp_server_paper/mcp_server.py"]
    }
  }
}

Available MCP Tools

Tool: search_papers

query: str          — Search terms (e.g. "AI agents")
max_results: int    — Papers to fetch (default 5, max 20)

Returns formatted text with all paper details.

Tool: summarize_text

text: str    — Source text (e.g. abstract)
query: str   — Query terms for relevance scoring

Tool: get_keywords

text: str     — Source text
top_n: int    — Number of keywords (default 5)

Tool: create_citation

authors: list[str]  — Author names
year: str           — Publication year
title: str          — Paper title

How It Works

1. Search (search_arxiv)

Queries the arXiv API via the arxiv Python library, fetching title, authors, published year, abstract, and link for each result.

2. Ranking (rank_papers)

Tokenizes the query into words and counts how often each word appears in title + summary. Papers are sorted descending by total score.

3. Smart Summary (smart_summary)

Splits the abstract into sentences, scores each sentence by query-word presence, and returns the top 2.

4. Keyword Extraction (extract_keywords)

• Removes punctuation

• Keeps words with length > 6

• Filters a manually defined stopword set

• Returns the top 5 by frequency

5. Citation Generator (generate_citation)

Formats: Author1, Author2, and Author3 (2025). Paper Title.

Testing

Install dependencies, then run the full test suite:

pip install -r requirements.txt
pytest tests/ -v

Test coverage

Run only fast unit tests (no network):

pytest tests/test_research.py -v

Run live integration tests (requires network):

pytest tests/test_integration.py -v

Architecture

flowchart TD
    CLI[main.py CLI] --> RE[research.py]
    MCP[mcp_server.py MCP] --> RE
    RE --> ARXIV[arXiv API]
    RE --> RANK[rank_papers]
    RE --> SUM[smart_summary]
    RE --> KW[extract_keywords]
    RE --> CITE[generate_citation]

Both entry points share the same research.py engine. The CLI prints formatted output to stdout; the MCP server returns the same formatted strings as tool results over stdio JSON-RPC.

Constraints

• No OpenAI or external AI APIs

• No transformers or ML models

• No API keys required

• Everything runs locally except the arXiv network fetch

License

MIT (or your preferred license)