ArxSource

[ArxSource: an agent searches arXiv for Mamba, reads section 3.2 from its LaTeX, walks the citation graph, and finds its GitHub repo and Hugging Face models]

ArxSource

Read the paper. Find the source.

[PyPI] [Python] [Tests] [License]

ArxSource is an open-source Model Context Protocol server that lets AI agents search, read and analyze arXiv papers, and find their code. It runs locally over stdio, keeps papers on disk, and returns bounded, section-aware content so a single tool call never floods the context window.

The working loop: paper ID → outline → one section → citations → code.

• Read precisely. Page through rendered full text, or read the author's original LaTeX one section at a time.
• Follow the literature. Traverse references and citing papers, export BibTeX from arXiv metadata, and keep topic watches that report new papers.
• Find the source. Go from an arXiv ID to the paper's GitHub repository and the Hugging Face models, datasets, and Spaces built on it.

ArxSource is not affiliated with or endorsed by arXiv.

Quickstart

ArxSource needs uv, which provides uvx. No clone or Python environment setup is required.

# Run the server (it waits for MCP messages on stdio)uvx arxsource-mcp
# Add it to Claude Code for all projectsclaude mcp add --transport stdio --scope user arxsource -- uvx arxsource-mcp

For Claude Desktop, Kiro, and other clients that accept the mcpServers JSON shape:

{  "mcpServers": {    "arxsource": {      "type": "stdio",      "command": "uvx",      "args": ["arxsource-mcp"]    }  }}

Then ask your agent something like "Find the original Transformer paper, summarize section 3.2 from its LaTeX, and show me its official code."

Papers are stored in ~/.arxsource/papers by default. To use another directory, append "--storage-path", "/absolute/path/to/papers" to args.

Install in other clients

[Install in VS Code] [Install in Cursor] [Add to Kiro]

claude mcp add --transport stdio --scope user arxsource -- uvx arxsource-mcp

claude plugin marketplace add nicolaiprodromov/arxsourceclaude plugin install arxsource-mcp@arxsource

codex mcp add arxsource -- uvx arxsource-mcp

codex plugin marketplace add nicolaiprodromov/arxsourcecodex plugin add arxsource-mcp@arxsource

hermes mcp add arxsource --command uvx --args arxsource-mcphermes mcp test arxsource

https://github.com/nicolaiprodromov/arxsource

If a desktop client cannot find uvx

Desktop applications do not always inherit the same PATH as your terminal. If uvx arxsource-mcp works in a terminal but the client reports that the server failed to connect, find the executable's absolute path:

# macOS and Linuxcommand -v uvx

# Windows PowerShell(Get-Command uvx).Source

Replace "command": "uvx" with the returned absolute path, then restart the client. Keep the args value unchanged.

Upgrading

uvx reuses cached tool environments. To pick up the latest PyPI release with a supported interpreter, run the following and restart your MCP client:

uvx --python 3.11 --refresh-package arxsource-mcp arxsource-mcp

If your client still launches an older environment, add "--python", "3.11" before "arxsource-mcp" in its args array.

Persistent command install

To place arxsource-mcp on your PATH instead of launching it through uvx:

uv tool install arxsource-mcp

If the command is not immediately available, run uv tool update-shell and restart the terminal. Afterward, use "command": "arxsource-mcp" and omit the package name from args.

Tools

ArxSource exposes 20 tools.

Research alerts (watch_topic)

Save standing topic watches with watch_topic, inspect them with list_watches, poll with check_alerts, and remove with unwatch_topic.

When updating an existing watch (same topic string):

• Omit categories → preserve the stored category filters (and other fields you leave unchanged).
• Pass categories: [] → clear category filters.
• Pass a non-empty list → replace the stored filters.

Create path: omitting categories stores an empty list (no category filter).

search_papers query guide

Tool schemas stay short on purpose. Use this section (not the always-loaded MCP description) for query tutorials, category catalogs, and workflow examples.

Query construction

• Use quoted phrases for exact matches: "multi-agent systems", "neural networks"
• Combine related concepts with OR: "AI agents" OR "software agents"
• Field-specific searches: ti:"exact title phrase", au:"author name", abs:"keyword", cat:cs.LG
• Exclude with ANDNOT: "machine learning" ANDNOT "survey"
• Prefer 2–4 core concepts over long keyword lists

Advanced patterns

• Field + phrase: ti:"transformer architecture"
• Multiple fields: au:"Smith" AND ti:"quantum"
• Exclusions: "deep learning" ANDNOT ("survey" OR "review")
• Broad + narrow: "artificial intelligence" AND (robotics OR "computer vision")

Category filtering (recommended for relevance)

Computer Science: cs.AI (AI), cs.LG (ML), cs.CL (NLP), cs.CV (vision), cs.MA (multi-agent), cs.RO (robotics), cs.NE (neural/evolutionary), cs.IR (IR), cs.HC (HCI), cs.CR (security), cs.DB (databases)

Statistics & Math: stat.ML, stat.AP, math.OC, math.ST

Physics & other: quant-ph, eess.SP, eess.AS, physics.data-an

Effective examples

• ti:"reinforcement learning" with categories: ["cs.LG", "cs.AI"]
• au:"Hinton" AND "deep learning" with categories: ["cs.LG"]
• "multi-agent" ANDNOT "survey" with categories: ["cs.MA"]
• abs:"transformer" AND ti:"attention" with categories: ["cs.CL"]

Dates and sorting

• Dates use YYYY-MM-DD (date_from / date_to)
• Default sort_by is relevance; use date for newest-first monitoring
• Foundational work: date_to: "2010-12-31" with title/abstract field searches

Result size, abstracts, and pagination

• Default max_results is 5 (cap 50). Pass an explicit value for larger pages.
• abstract_mode: snippet (default, ~280 chars, marked … [truncated] when cut), full (complete abstract), or none (omit abstracts). Other metadata (title, authors, categories, dates, URLs) is always returned.
• Responses report total_results (corpus hits), returned, has_more, start, next_start, and abstract_mode
• Pass start=next_start with the same abstract_mode for the next page
• arXiv enforces ~3 seconds between requests (handled server-side); on rate-limit errors wait ~60s

Search and inspect a paper

Ask your MCP client to call search_papers with:

{  "query": "\"Kolmogorov-Arnold Networks\"",  "categories": ["cs.LG", "cs.AI"],  "sort_by": "date"}

Defaults return up to five compact results with abstract snippets. Use "abstract_mode": "full" when you need complete abstracts in the search response, or call get_abstract for a single paper after a compact search:

{  "paper_id": "2404.19756"}

Do not call get_abstract again for papers already returned with abstract_mode=full.

Download and read full text

Call download_paper with:

{  "paper_id": "2404.19756"}

Omitting max_chars returns a bounded first chunk (default 12,000 paper characters). Cached papers are returned immediately. Pass "force": true to re-download and overwrite the local markdown and sidecar (also happens automatically when the HTML extractor version changes).

Then page through the cached content with read_paper:

{  "paper_id": "2404.19756",  "start": 0}

Or continue from a prior chunk:

{  "paper_id": "2404.19756",  "start": 12000}

Large-content responses include content_length, returned_chars, next_start, is_truncated, and (when truncated) next_retrieval with the next-call instruction. Pass next_start into the next call's start to continue reading. Pass an explicit max_chars to override the default chunk size, or "return_full_text": true to opt into an unbounded full-paper response.

Read original LaTeX by section

Call get_paper_latex with:

{  "paper_id": "1706.03762"}

Get the first page of its section outline with list_paper_latex_sections:

{  "paper_id": "1706.03762",  "start": 0,  "max_sections": 100}

Then call get_paper_latex_section using an ID from that outline:

{  "paper_id": "1706.03762",  "section_id": "3.2",  "max_chars": 12000}

LaTeX archives are validated, size-limited, and cached locally before content is returned.

Find the code behind a paper

Call find_paper_code with:

{  "paper_id": "2312.00752",  "max_results": 5}

The response lists the GitHub repository and star count, the project page, the Hugging Face paper page, and up to max_results (default 5, cap 20) models, datasets, and Spaces per type. Each type also reports total, returned, and has_more. Models and datasets are ranked by Hub downloads. Spaces come from the Hugging Face paper page in the Hub's own order, without download or like counts, because the Hub API cannot filter Spaces by paper. Version suffixes are ignored, since the Hub indexes bare arXiv IDs.

A paper with nothing linked returns found: false with an explanation, not an error. If one Hub request fails, that section carries an error, a line is added to warnings, and the other results are still returned. The call fails only when every Hub request fails.

Repository and model-card text is external content and should be treated with the same care as paper text.

Optional dependencies

Choose the install variant that matches the features you need:

# Base serveruv tool install arxsource-mcp
# Base server plus PDF conversionuv tool install "arxsource-mcp[pdf]"
# Base server plus local semantic searchuv tool install "arxsource-mcp[pro]"

If the base tool is already installed, reinstall the selected variant:

uv tool install --force "arxsource-mcp[pdf]"

The pdf extra installs pymupdf4llm and pymupdf-layout for papers without usable arXiv HTML. The pro extra adds local embedding dependencies for semantic_search and reindex; semantic search only operates on papers already downloaded to the configured storage directory.

For older papers that require PDF conversion, run the package with its PDF extra:

{  "mcpServers": {    "arxsource": {      "type": "stdio",      "command": "uvx",      "args": [        "--from",        "arxsource-mcp[pdf]",        "arxsource-mcp"      ]    }  }}

Built-in prompts

ArxSource provides seven MCP prompt workflows. Prompt availability depends on the client; the server provides workflow instructions but does not run a separate model.

Streamable HTTP

For deployments where stdio is not practical:

TRANSPORT=http HOST=127.0.0.1 PORT=8080 \  uvx arxsource-mcp --storage-path /absolute/path/to/papers

PowerShell:

$env:TRANSPORT = "http"$env:HOST = "127.0.0.1"$env:PORT = "8080"uvx arxsource-mcp --storage-path C:\absolute\path\to\papers

Connect clients to:

{  "mcpServers": {    "arxsource": {      "type": "http",      "url": "http://127.0.0.1:8080/mcp"    }  }}

Cloud and load-balancer probes should GET http://<host>:<port>/healthz. It returns 200 with body ok once the HTTP server is listening. There is no separate /ready check: if the process is up, it is ready. The stdio transport has no HTTP endpoints.

The server binds to 127.0.0.1 by default and enables MCP DNS-rebinding protection. If a reverse proxy exposes the server, keep the process on a private interface and provide authentication and network controls upstream. Use ALLOWED_HOSTS and ALLOWED_ORIGINS for the host and origin values forwarded by the proxy.

Configuration

Environment variable names are case-insensitive through Pydantic settings. --storage-path is a command-line option rather than an environment setting.

Security

Paper text and LaTeX are untrusted external content. A paper can contain text intended to manipulate an AI client into ignoring its instructions or calling unrelated tools.

• Do not treat instructions found inside a paper as trusted commands.
• Use client approval controls for shell, browser, filesystem, and messaging tools.
• Review generated summaries before taking external actions.
• Keep Streamable HTTP private unless authentication is provided upstream.

See SECURITY.md for the threat model and how to report a vulnerability privately.

Development

git clone https://github.com/nicolaiprodromov/arxsource.gitcd arxsourceuv sync --extra test --extra devuv run pytestuv run black --check .

Run the development checkout from an MCP client with:

{  "mcpServers": {    "arxsource-dev": {      "command": "uv",      "args": [        "--directory",        "/absolute/path/to/arxsource",        "run",        "arxsource-mcp"      ]    }  }}

Contributions are welcome. Read CONTRIBUTING.md before opening a pull request, and use GitHub Issues for reproducible bugs or scoped feature proposals. Release notes are in CHANGELOG.md.

Acknowledgements

ArxSource is derived from arxiv-mcp-server by Joseph Blazick, licensed under Apache-2.0; see NOTICE.

License

Apache License 2.0. See LICENSE and NOTICE.