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https://github.com/blazickjp/arxiv-mcp-server

A Model Context Protocol server for searching and analyzing arXiv papers
https://github.com/blazickjp/arxiv-mcp-server

ai arxiv claude-ai gpt llm mcp-server model-context-protocol papers python research

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A Model Context Protocol server for searching and analyzing arXiv papers

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# ArXiv MCP Server



> 🔍 Enable AI assistants to search and access arXiv papers through a simple MCP interface.



The ArXiv MCP Server provides a bridge between AI assistants and arXiv's research repository through the Model Context Protocol (MCP). It allows AI models to search for papers and access their content in a programmatic way.





  

🤝 **[Contribute](https://github.com/blazickjp/arxiv-mcp-server/blob/main/CONTRIBUTING.md)** • 

📝 **[Report Bug](https://github.com/blazickjp/arxiv-mcp-server/issues)**



Pulse MCP Badge




## ✨ Core Features



- 🔎 **Paper Search**: Query arXiv papers with filters for date ranges and categories

- 📄 **Paper Access**: Download and read paper content

- 📋 **Paper Listing**: View all downloaded papers

- 🗃️ **Local Storage**: Papers are saved locally for faster access

- 📝 **Prompts**: A set of research prompts for paper analysis



## 🔒 Security



### Prompt Injection Risk



**Paper content retrieved from arXiv is untrusted external input.**



When an AI assistant downloads or reads a paper through this server, the paper's

text is passed directly into the model's context. A maliciously crafted paper

could embed adversarial instructions designed to hijack the AI's behavior — for

example, instructing it to exfiltrate data, invoke other tools with unintended

arguments, or override system-level instructions. This is a known class of

attack described by OWASP as **LLM01: Prompt Injection** and by the OWASP

Agentic AI framework as **AG01: Prompt Injection in LLM-Integrated Systems**.



### Recommended Mitigations



1. **Use read-only MCP configurations** — where possible, configure the MCP

   client so that the arxiv-mcp-server cannot trigger write operations or invoke

   other tools on your behalf.

2. **Review paper content before acting on AI summaries** — if an AI summary

   asks you to run commands or visit external URLs that were not part of your

   original request, treat that as a red flag.

3. **Be cautious in multi-tool setups** — agentic pipelines that combine this

   server with filesystem, shell, or browser tools are higher risk; a prompt

   injection in a paper could chain tool calls unexpectedly.

4. **Treat AI-generated summaries as data, not instructions** — always apply

   human judgment before executing any action the AI recommends after reading a

   paper.



### References



- [OWASP LLM01: Prompt Injection](https://owasp.org/www-project-top-10-for-large-language-model-applications/)

- [OWASP Agentic AI - AG01: Prompt Injection](https://genai.owasp.org/llmrisk/ag01-prompt-injection/)



---



## 🚀 Quick Start



### Installing via Smithery



To install ArXiv Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/arxiv-mcp-server):



```bash

npx -y @smithery/cli install arxiv-mcp-server --client claude

```



### Installing via Claude Desktop (.mcpb)



The `.mcpb` bundle is the one-click install path for Claude Desktop on macOS. It bundles the server code and Python package dependencies, so users do not need `uv`, `pip`, or manual MCP JSON configuration. Python 3.11+ must still be available on the user's machine.



1. Download the artifact matching your Mac from the [latest release](https://github.com/blazickjp/arxiv-mcp-server/releases/latest):

   - Apple Silicon: `arxiv-mcp-server-darwin-arm64-.mcpb`

   - Intel: `arxiv-mcp-server-darwin-x86_64-.mcpb`

2. In Claude Desktop open **Settings → Extensions** (or drag-and-drop the file onto the Claude Desktop window).

3. Click **Install** and, when prompted, set your preferred paper storage directory (defaults to `~/.arxiv-mcp-server/papers`).



Claude Desktop launches the bundled server over stdio — no configuration file edits needed.



### Installing Manually



> **Important — use `uv tool install`, not `uv pip install`**

>

> Running `uv pip install arxiv-mcp-server` installs the package into the

> current virtual environment but does **not** place the `arxiv-mcp-server`

> executable on your `PATH`.  You must use `uv tool install` so that uv

> creates an isolated environment and exposes the executable globally:



```bash

uv tool install arxiv-mcp-server

```



After this, the `arxiv-mcp-server` command will be available on your `PATH`.



> **PDF fallback (older papers):** Most arXiv papers have an HTML version which

> the base install handles automatically. For older papers that only have a PDF,

> the server needs the `[pdf]` extra (pymupdf4llm). Install it with:

>

> ```bash

> uv tool install 'arxiv-mcp-server[pdf]'

> ```

You can verify it with:



```bash

arxiv-mcp-server --help

```



If you previously ran `uv pip install arxiv-mcp-server` and the command is

missing, uninstall it and re-install with `uv tool install` as shown above.



For development:



```bash

# Clone and set up development environment

git clone https://github.com/blazickjp/arxiv-mcp-server.git

cd arxiv-mcp-server



# Create and activate virtual environment

uv venv

source .venv/bin/activate



# Install with test dependencies (development only — no global executable)

uv pip install -e ".[test]"

```



### 🤖 Codex Plugin Integration



This repository now includes a Codex plugin manifest at `.codex-plugin/plugin.json`

and a portable MCP config at `.mcp.json` so Codex-oriented tooling can discover

the server without inventing its own install recipe.



The Codex integration uses the same stdio launch path documented elsewhere in

this README:



```json

{

  "mcpServers": {

    "arxiv": {

      "command": "uvx",

      "args": ["arxiv-mcp-server"]

    }

  }

}

```



If your Codex client supports plugin manifests, point it at

`./.codex-plugin/plugin.json`. If it only supports raw MCP configuration, use

`./.mcp.json` directly.



### 🔌 MCP Integration



Add this configuration to your MCP client config file:



```json

{

    "mcpServers": {

        "arxiv-mcp-server": {

            "command": "uv",

            "args": [

                "tool",

                "run",

                "arxiv-mcp-server",

                "--storage-path", "/path/to/paper/storage"

            ]

        }

    }

}

```



For Development:



```json

{

    "mcpServers": {

        "arxiv-mcp-server": {

            "command": "uv",

            "args": [

                "--directory",

                "path/to/cloned/arxiv-mcp-server",

                "run",

                "arxiv-mcp-server",

                "--storage-path", "/path/to/paper/storage"

            ]

        }

    }

}

```



### HTTP Transport



For server deployments where stdio is not practical, run the server with Streamable HTTP:



```bash

TRANSPORT=http HOST=127.0.0.1 PORT=8080 arxiv-mcp-server --storage-path /path/to/papers

```



Then configure an MCP client that supports Streamable HTTP:



```json

{

    "mcpServers": {

        "arxiv-mcp-server": {

            "type": "http",

            "url": "http://127.0.0.1:8080/mcp"

        }

    }

}

```



The default HTTP bind host is `127.0.0.1`. Streamable HTTP enables MCP DNS rebinding protection by default and allows loopback hosts for the configured port. If exposing the server through a reverse proxy, keep it bound to localhost unless you have added authentication and network controls upstream; set `ALLOWED_HOSTS` and `ALLOWED_ORIGINS` to the external host/origin values your proxy forwards.



## 🔒 Security Note



arXiv papers are user-generated, untrusted content. Paper text returned by this

server may contain prompt injection attempts — crafted text designed to manipulate

an AI assistant's behavior. Treat all paper content as untrusted input.



In production environments, apply appropriate sandboxing and avoid feeding raw

paper content into agentic pipelines that have access to sensitive tools or data

without review. See [SECURITY.md](SECURITY.md) for the full security policy.



## 💡 Available Tools



### Core Workflow



The typical workflow for deep paper research is:



```

search_papers → download_paper → read_paper

```



`list_papers` shows what you have locally. `semantic_search` searches across your local collection.



---



### 1. Paper Search

Search arXiv with optional category, date, and boolean filters. Enforces arXiv's 3-second rate limit automatically. If rate limited, wait 60 seconds before retrying.



```python

result = await call_tool("search_papers", {

    "query": "\"KAN\" OR \"Kolmogorov-Arnold Networks\"",

    "max_results": 10,

    "date_from": "2024-01-01",

    "categories": ["cs.LG", "cs.AI"],

    "sort_by": "date"   # or "relevance" (default)

})

```



Supported categories include `cs.AI`, `cs.LG`, `cs.CL`, `cs.CV`, `cs.NE`, `stat.ML`, `math.OC`, `quant-ph`, `eess.SP`, and more. See tool description for the full list.



### 2. Paper Download

Download a paper by its arXiv ID. Tries HTML first, falls back to PDF. Stores the paper locally for `read_paper` and `semantic_search`.



```python

result = await call_tool("download_paper", {

    "paper_id": "2401.12345"

})

```



> For older papers that only have a PDF, install the `[pdf]` extra: `uv tool install 'arxiv-mcp-server[pdf]'`



### 3. List Papers

List all papers downloaded locally. Returns arXiv IDs only — use `read_paper` to access content.



```python

result = await call_tool("list_papers", {})

```



### 4. Read Paper

Read the full text of a locally downloaded paper in markdown. **Requires `download_paper` to be called first.**



```python

result = await call_tool("read_paper", {

    "paper_id": "2401.12345"

})

```



## 📝 Research Prompts



The server offers specialized prompts to help analyze academic papers:



### Paper Analysis Prompt

A comprehensive workflow for analyzing academic papers that only requires a paper ID:



```python

result = await call_prompt("deep-paper-analysis", {

    "paper_id": "2401.12345"

})

```



This prompt includes:

- Detailed instructions for using available tools (list_papers, download_paper, read_paper, search_papers)

- A systematic workflow for paper analysis

- Comprehensive analysis structure covering:

  - Executive summary

  - Research context

  - Methodology analysis

  - Results evaluation

  - Practical and theoretical implications

- Future research directions

- Broader impacts



### Pro Prompt Pack



- `summarize_paper`: concise structured summary for one paper.

- `compare_papers`: side-by-side technical comparison across paper IDs.

- `literature_review`: thematic synthesis across a topic and optional paper set.



## ⚙️ Configuration



Configure through command-line options and environment variables:



| Setting | Purpose | Default |

|---------|---------|---------|

| `--storage-path` | Paper storage location | `~/.arxiv-mcp-server/papers` |

| `MAX_RESULTS` | Maximum search results | `50` |

| `REQUEST_TIMEOUT` | API timeout in seconds | `60` |

| `TRANSPORT` | Transport type: `stdio`, `http`, or `streamable-http` | `stdio` |

| `HOST` | Host to bind to in HTTP mode | `127.0.0.1` |

| `PORT` | Port to listen on in HTTP mode | `8000` |

| `ALLOWED_HOSTS` | Comma-separated extra allowed Host header values for Streamable HTTP DNS rebinding protection | empty |

| `ALLOWED_ORIGINS` | Comma-separated extra allowed Origin header values for Streamable HTTP DNS rebinding protection | empty |



## 🧪 Testing



Run the test suite:



```bash

python -m pytest

```



## 🧪 Experimental Features



> **These features are not yet fully tested and may behave unexpectedly. Use with caution.**



The following tools require additional dependencies and are under active development:



```bash

uv pip install -e ".[pro]"

```



### Semantic Search

Semantic similarity search over your **locally downloaded** papers only. Returns empty results if no papers have been downloaded yet. Requires `[pro]` dependencies.



```python

result = await call_tool("semantic_search", {

    "query": "test-time adaptation in multimodal transformers",

    "max_results": 5

})

# or find papers similar to a known paper:

result = await call_tool("semantic_search", {

    "paper_id": "2404.19756",

    "max_results": 5

})

```



### Citation Graph

Fetch references and citing papers via Semantic Scholar. Works on any arXiv ID — no local download required.



```python

result = await call_tool("citation_graph", {

    "paper_id": "2401.12345"

})

```



### Research Alerts

Save topic watches and poll for newly published papers since the last check. Uses the same query syntax as `search_papers`.



```python

# Register a watch (idempotent — calling again updates the existing watch)

await call_tool("watch_topic", {

    "topic": "\"multi-agent reinforcement learning\"",

    "categories": ["cs.AI", "cs.LG"],

    "max_results": 10

})



# Check all watches — returns only papers published since last check

result = await call_tool("check_alerts", {})



# Check a single watch

result = await call_tool("check_alerts", {"topic": "\"multi-agent reinforcement learning\""})

```



### Advanced Prompts

`summarize_paper`, `compare_papers`, and `literature_review` for deeper research workflows. Requires `[pro]` dependencies.



---



## 📄 License



Released under the Apache License 2.0. See the LICENSE file for details.



---






Made with ❤️ by the Pearl Labs Team



ArXiv Server MCP server