{"id":"01fe1a05-e6e4-45cf-b38f-7367d9c83477","slug":"arxsource","kind":"mcp","name":"arxsource","summary":"Read the paper. Find the source. An open-source MCP server for searching, reading and analyzing arXiv papers, and finding their code.","owner":"nicolaiprodromov","repoUrl":"https://github.com/nicolaiprodromov/arxsource","license":"Apache-2.0","licenseTier":"permissive","trust":"community","stars":2,"match":null,"fewStars":true,"format":"mcp","createdAt":"2026-11-06T14:53:34.000Z","addedAt":"2026-11-08T04:48:32.998Z","logoUrl":"https://avatars.githubusercontent.com/u/57862822?u=a91daa9badf79d4206c26ecb5f93763808c6702c&v=4","linkOnly":false,"collection":null,"skillPath":null,"categories":["cloud-infra","developer-tools","memory-knowledge"],"tags":["ai-agents","arxiv","huggingface","latex","research"],"runtime":"python","local":true,"cloudCapable":false,"cloudCapability":"unknown","cloudReadyOnPlatform":false,"needsApiKey":false,"reviewed":false,"syncedAt":"2026-11-08T04:51:01.490Z","sample":false,"url":"https://conduits.dev/tools/arxsource","language":"Python","transports":["stdio"],"install":{"pypi":"arxsource-mcp"},"requirements":{"env":[{"name":"ARXSOURCE_STORAGE_PATH","secret":false,"required":false,"description":"Optional path for storing downloaded papers locally."}],"upstreamMayCharge":false},"tools":null,"readme":"
\n \n
\n\n# ArxSource\n\n**Read the paper. Find the source.**\n\n\n\n[](https://pypi.org/project/arxsource-mcp/)\n[](https://pypi.org/project/arxsource-mcp/)\n[](https://github.com/nicolaiprodromov/arxsource/actions/workflows/tests.yml)\n[](https://github.com/nicolaiprodromov/arxsource/blob/main/LICENSE)\n\nArxSource is an open-source [Model Context Protocol](https://modelcontextprotocol.io) 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.\n\nThe working loop: **paper ID → outline → one section → citations → code.**\n\n- **Read precisely.** Page through rendered full text, or read the author's original LaTeX one section at a time.\n- **Follow the literature.** Traverse references and citing papers, export BibTeX from arXiv metadata, and keep topic watches that report new papers.\n- **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.\n\nArxSource is not affiliated with or endorsed by arXiv.\n\n## Quickstart\n\nArxSource needs [uv](https://docs.astral.sh/uv/getting-started/installation/), which provides `uvx`. No clone or Python environment setup is required.\n\n```bash\n# Run the server (it waits for MCP messages on stdio)\nuvx arxsource-mcp\n\n# Add it to Claude Code for all projects\nclaude mcp add --transport stdio --scope user arxsource -- uvx arxsource-mcp\n```\n\nFor Claude Desktop, Kiro, and other clients that accept the `mcpServers` JSON shape:\n\n```json\n{\n \"mcpServers\": {\n \"arxsource\": {\n \"type\": \"stdio\",\n \"command\": \"uvx\",\n \"args\": [\"arxsource-mcp\"]\n }\n }\n}\n```\n\nThen ask your agent something like *\"Find the original Transformer paper, summarize section 3.2 from its LaTeX, and show me its official code.\"*\n\nPapers are stored in `~/.arxsource/papers` by default. To use another directory, append `\"--storage-path\", \"/absolute/path/to/papers\"` to `args`.\n\n## Install in other clients\n\n[](https://vscode.dev/redirect/mcp/install?name=arxsource&config=%7B%22type%22%3A%22stdio%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22arxsource-mcp%22%5D%7D)\n[](https://cursor.com/en/install-mcp?name=arxsource&config=eyJ0eXBlIjoic3RkaW8iLCJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJhcnhzb3VyY2UtbWNwIl19)\n[](https://kiro.dev/launch/mcp/add?name=arxsource&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22arxsource-mcp%22%5D%2C%22disabled%22%3Afalse%2C%22autoApprove%22%3A%5B%5D%7D)\n\n\nPer-client recipes (Claude Code, Codex, Hermes, VS Code / Kiro, Claude Desktop, plugins)\n\nUse the JSON above unless your client has a one-line helper. Other clients may use a top-level `servers` object, TOML, or their own settings UI; consult the client's MCP documentation.\n\n### Claude Code\n\nAdd the MCP server for all projects:\n\n```bash\nclaude mcp add --transport stdio --scope user arxsource -- uvx arxsource-mcp\n```\n\nFor the plugin integration, which installs the MCP connection plus the bundled ArxSource research skill, register this repository as a marketplace and install the plugin:\n\n```bash\nclaude plugin marketplace add nicolaiprodromov/arxsource\nclaude plugin install arxsource-mcp@arxsource\n```\n\nVerify the direct MCP installation with `claude mcp get arxsource`. Restart Claude Code or run `/reload-plugins` after installing the plugin.\n\n### OpenAI Codex\n\nAdd the MCP server:\n\n```bash\ncodex mcp add arxsource -- uvx arxsource-mcp\n```\n\nOr install the MCP connection and bundled research skill as a Codex plugin:\n\n```bash\ncodex plugin marketplace add nicolaiprodromov/arxsource\ncodex plugin add arxsource-mcp@arxsource\n```\n\nVerify the direct MCP installation with `codex mcp get arxsource`. Codex CLI, the Codex IDE extension, and Codex in the ChatGPT desktop app share this MCP configuration.\n\n### Hermes Agent\n\nAdd the server, approve the discovered tools, and test the saved connection:\n\n```bash\nhermes mcp add arxsource --command uvx --args arxsource-mcp\nhermes mcp test arxsource\n```\n\n### VS Code, Cursor, and Kiro\n\nUse the install buttons above. For the Kiro Power integration, open the **Powers** panel, choose **Add Custom Power → Import power from GitHub**, and enter:\n\n```text\nhttps://github.com/nicolaiprodromov/arxsource\n```\n\nThe Power installs the MCP connection from `mcp.json` and adds focused research guidance. To configure Kiro manually, place the JSON above in `.kiro/settings/mcp.json` for one workspace or `~/.kiro/settings/mcp.json` for all workspaces.\n\n### Claude Desktop bundle\n\nmacOS users can install a bundled `.mcpb` extension from the [latest GitHub release](https://github.com/nicolaiprodromov/arxsource/releases/latest): `arxsource-mcp-darwin-arm64-.mcpb` for Apple Silicon or `arxsource-mcp-darwin-x86_64-.mcpb` for Intel.\n\nDouble-click the bundle, drag it into Claude Desktop, or open **Settings → Extensions → Advanced settings → Install Extension…**. The bundle includes the server dependencies and requires CPython 3.11.x.\n\n### Plugin manifests\n\nThe same MCP server and research skill are packaged for each plugin system:\n\n| Integration | Manifest | Marketplace |\n|---|---|---|\n| Claude Code | `.claude-plugin/plugin.json` | `.claude-plugin/marketplace.json` |\n| OpenAI Codex / ChatGPT Work | `.codex-plugin/plugin.json` | `.agents/plugins/marketplace.json` |\n| Kiro Power | `POWER.md` | `mcp.json` |\n| Shared MCP launch | `.mcp.json` for Claude and repository-local clients; `.codex-mcp.json` for Codex plugins | `uvx arxsource-mcp` |\n| Shared research workflow | `skills/arxsource/SKILL.md` | Installed with either plugin |\n\n\n\n### If a desktop client cannot find `uvx`\n\nDesktop 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:\n\n```bash\n# macOS and Linux\ncommand -v uvx\n```\n\n```powershell\n# Windows PowerShell\n(Get-Command uvx).Source\n```\n\nReplace `\"command\": \"uvx\"` with the returned absolute path, then restart the client. Keep the `args` value unchanged.\n\n### Upgrading\n\n`uvx` reuses cached tool environments. To pick up the latest PyPI release with a supported interpreter, run the following and restart your MCP client:\n\n```bash\nuvx --python 3.11 --refresh-package arxsource-mcp arxsource-mcp\n```\n\nIf your client still launches an older environment, add `\"--python\", \"3.11\"` before `\"arxsource-mcp\"` in its `args` array.\n\n### Persistent command install\n\nTo place `arxsource-mcp` on your `PATH` instead of launching it through `uvx`:\n\n```bash\nuv tool install arxsource-mcp\n```\n\nIf 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`.\n\n## Tools\n\nArxSource exposes 20 tools.\n\n| Tool | Purpose | Notes |\n|---|---|---|\n| `search_papers` | Search arXiv by query, category, date, and sort order | Default ≤5 compact results (`abstract_mode=snippet`); remote arXiv API |\n| `get_abstract` | Fetch metadata and an abstract by arXiv ID | Does not download the paper |\n| `download_paper` | Download and convert a paper to local Markdown | HTML first; PDF fallback uses `[pdf]`; `force=true` re-fetches; content bounded to 12,000 chars by default |\n| `list_papers` | List papers stored locally | Returns id, title, authors, published; `compact` for IDs only |\n| `read_paper` | Read locally stored paper content | Bounded to 12,000 chars by default; supports `start`/`max_chars`/`return_full_text` |\n| `get_paper_outline` | Paginated markdown heading outline | Stable hierarchical section IDs |\n| `read_paper_section` | Read one bounded markdown section | By outline ID or unique title |\n| `search_paper_text` | Bounded passage search in a paper | Source offsets; no Torch required |\n| `get_paper_latex` | Retrieve bounded author-submitted LaTeX | Remote arXiv source archive |\n| `list_paper_latex_sections` | Return a paginated LaTeX outline | Supports `start` and `max_sections` |\n| `get_paper_latex_section` | Read one bounded LaTeX section | Select by outline ID or exact title |\n| `citation_graph` | Fetch references and citing papers | Remote Semantic Scholar API (1 call per paper, cached on disk); optional free API key improves reliability |\n| `export_citations` | Export BibTeX for one or more arXiv IDs | Authoritative arXiv metadata |\n| `find_paper_code` | Find a paper's code and Hugging Face artifacts | Input: `paper_id`, optional `max_results` (1-20, default 5). Returns the GitHub repository and star count, project page, and top Hugging Face models and datasets (by downloads) plus linked Spaces. Remote Hugging Face Hub API (no key); returns `structuredContent` |\n| `watch_topic` | Save or update an arXiv topic watch | Stored locally; omit `categories` to preserve on update, `categories: []` to clear |\n| `list_watches` | List saved topic watches | Read-only; does not advance last_checked |\n| `check_alerts` | Check saved watches for new papers | Returns papers since the last check |\n| `unwatch_topic` | Delete a saved topic watch | Exact topic match; not-found if missing |\n| `semantic_search` | Search downloaded papers by semantic similarity | Requires `[pro]` |\n| `reindex` | Rebuild the local semantic index | Requires `[pro]` |\n\n### Research alerts (`watch_topic`)\n\nSave standing topic watches with `watch_topic`, inspect them with `list_watches`, poll with `check_alerts`, and remove with `unwatch_topic`.\n\nWhen updating an existing watch (same `topic` string):\n\n- **Omit** `categories` → **preserve** the stored category filters (and other fields you leave unchanged).\n- Pass **`categories: []`** → **clear** category filters.\n- Pass a non-empty list → replace the stored filters.\n\nCreate path: omitting `categories` stores an empty list (no category filter).\n\n### search_papers query guide\n\nTool schemas stay short on purpose. Use this section (not the always-loaded MCP description) for query tutorials, category catalogs, and workflow examples.\n\n**Query construction**\n\n- Use quoted phrases for exact matches: `\"multi-agent systems\"`, `\"neural networks\"`\n- Combine related concepts with OR: `\"AI agents\" OR \"software agents\"`\n- Field-specific searches: `ti:\"exact title phrase\"`, `au:\"author name\"`, `abs:\"keyword\"`, `cat:cs.LG`\n- Exclude with ANDNOT: `\"machine learning\" ANDNOT \"survey\"`\n- Prefer 2–4 core concepts over long keyword lists\n\n**Advanced patterns**\n\n- Field + phrase: `ti:\"transformer architecture\"`\n- Multiple fields: `au:\"Smith\" AND ti:\"quantum\"`\n- Exclusions: `\"deep learning\" ANDNOT (\"survey\" OR \"review\")`\n- Broad + narrow: `\"artificial intelligence\" AND (robotics OR \"computer vision\")`\n\n**Category filtering** (recommended for relevance)\n\nComputer 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)\n\nStatistics & Math: `stat.ML`, `stat.AP`, `math.OC`, `math.ST`\n\nPhysics & other: `quant-ph`, `eess.SP`, `eess.AS`, `physics.data-an`\n\n**Effective examples**\n\n- `ti:\"reinforcement learning\"` with `categories: [\"cs.LG\", \"cs.AI\"]`\n- `au:\"Hinton\" AND \"deep learning\"` with `categories: [\"cs.LG\"]`\n- `\"multi-agent\" ANDNOT \"survey\"` with `categories: [\"cs.MA\"]`\n- `abs:\"transformer\" AND ti:\"attention\"` with `categories: [\"cs.CL\"]`\n\n**Dates and sorting**\n\n- Dates use `YYYY-MM-DD` (`date_from` / `date_to`)\n- Default `sort_by` is `relevance`; use `date` for newest-first monitoring\n- Foundational work: `date_to: \"2010-12-31\"` with title/abstract field searches\n\n**Result size, abstracts, and pagination**\n\n- Default `max_results` is **5** (cap 50). Pass an explicit value for larger pages.\n- `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.\n- Responses report `total_results` (corpus hits), `returned`, `has_more`, `start`, `next_start`, and `abstract_mode`\n- Pass `start=next_start` with the same `abstract_mode` for the next page\n- arXiv enforces ~3 seconds between requests (handled server-side); on rate-limit errors wait ~60s\n\n### Search and inspect a paper\n\nAsk your MCP client to call `search_papers` with:\n\n```json\n{\n \"query\": \"\\\"Kolmogorov-Arnold Networks\\\"\",\n \"categories\": [\"cs.LG\", \"cs.AI\"],\n \"sort_by\": \"date\"\n}\n```\n\nDefaults 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:\n\n```json\n{\n \"paper_id\": \"2404.19756\"\n}\n```\n\nDo not call `get_abstract` again for papers already returned with `abstract_mode=full`.\n\n### Download and read full text\n\nCall `download_paper` with:\n\n```json\n{\n \"paper_id\": \"2404.19756\"\n}\n```\n\nOmitting `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).\n\nThen page through the cached content with `read_paper`:\n\n```json\n{\n \"paper_id\": \"2404.19756\",\n \"start\": 0\n}\n```\n\nOr continue from a prior chunk:\n\n```json\n{\n \"paper_id\": \"2404.19756\",\n \"start\": 12000\n}\n```\n\nLarge-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.\n\n### Read original LaTeX by section\n\nCall `get_paper_latex` with:\n\n```json\n{\n \"paper_id\": \"1706.03762\"\n}\n```\n\nGet the first page of its section outline with `list_paper_latex_sections`:\n\n```json\n{\n \"paper_id\": \"1706.03762\",\n \"start\": 0,\n \"max_sections\": 100\n}\n```\n\nThen call `get_paper_latex_section` using an ID from that outline:\n\n```json\n{\n \"paper_id\": \"1706.03762\",\n \"section_id\": \"3.2\",\n \"max_chars\": 12000\n}\n```\n\nLaTeX archives are validated, size-limited, and cached locally before content is returned.\n\n### Find the code behind a paper\n\nCall `find_paper_code` with:\n\n```json\n{\n \"paper_id\": \"2312.00752\",\n \"max_results\": 5\n}\n```\n\nThe 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.\n\nA 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.\n\nRepository and model-card text is external content and should be treated with the same care as paper text.\n\n## Optional dependencies\n\nChoose the install variant that matches the features you need:\n\n```bash\n# Base server\nuv tool install arxsource-mcp\n\n# Base server plus PDF conversion\nuv tool install \"arxsource-mcp[pdf]\"\n\n# Base server plus local semantic search\nuv tool install \"arxsource-mcp[pro]\"\n```\n\nIf the base tool is already installed, reinstall the selected variant:\n\n```bash\nuv tool install --force \"arxsource-mcp[pdf]\"\n```\n\nThe `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.\n\nFor older papers that require PDF conversion, run the package with its PDF extra:\n\n```json\n{\n \"mcpServers\": {\n \"arxsource\": {\n \"type\": \"stdio\",\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"arxsource-mcp[pdf]\",\n \"arxsource-mcp\"\n ]\n }\n }\n}\n```\n\n## Built-in prompts\n\nArxSource provides seven MCP prompt workflows. Prompt availability depends on the client; the server provides workflow instructions but does not run a separate model.\n\n| Prompt | Required arguments | Purpose |\n|---|---|---|\n| `research-discovery` | `topic` | Map terminology, searches, papers, research clusters, and a reading path |\n| `deep-paper-analysis` | `paper_id` | Analyze one paper in depth |\n| `summarize_paper` | `paper_id` | Summarize methods, results, and limitations |\n| `compare_papers` | `paper_ids` | Compare multiple papers |\n| `literature_review` | `topic` | Synthesize a topic and optional paper set |\n| `literature-synthesis` | `paper_ids` | Synthesize themes, methods, timelines, or gaps across papers |\n| `research-question` | `paper_ids`, `topic` | Formulate grounded, falsifiable research questions |\n\n## Streamable HTTP\n\nFor deployments where stdio is not practical:\n\n```bash\nTRANSPORT=http HOST=127.0.0.1 PORT=8080 \\\n uvx arxsource-mcp --storage-path /absolute/path/to/papers\n```\n\nPowerShell:\n\n```powershell\n$env:TRANSPORT = \"http\"\n$env:HOST = \"127.0.0.1\"\n$env:PORT = \"8080\"\nuvx arxsource-mcp --storage-path C:\\absolute\\path\\to\\papers\n```\n\nConnect clients to:\n\n```json\n{\n \"mcpServers\": {\n \"arxsource\": {\n \"type\": \"http\",\n \"url\": \"http://127.0.0.1:8080/mcp\"\n }\n }\n}\n```\n\nCloud and load-balancer probes should GET `http://:/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.\n\nThe 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.\n\n## Configuration\n\n| Setting | Default | Purpose |\n|---|---:|---|\n| `--storage-path` | `~/.arxsource/papers` | Paper, source-cache, alert, and index storage |\n| `MAX_RESULTS` | `50` | Server-side cap for result counts |\n| `ARXSOURCE_REQUEST_TIMEOUT` | `30` | Per-attempt read timeout in seconds for all arXiv requests (API, HTML, PDF, LaTeX). Values ≤ 0 fall back to default. |\n| `REQUEST_TIMEOUT` | – | Unprefixed fallback for `ARXSOURCE_REQUEST_TIMEOUT`, used only when the prefixed variable is not set. Values ≤ 0 fall back to the 30s default. |\n| `ARXSOURCE_CONNECT_TIMEOUT` | `10` | Connection timeout in seconds for all arXiv requests. Values ≤ 0 fall back to default. |\n| `ARXSOURCE_MAX_TOTAL_TIME` | `50` | Total budget in seconds for each tool call (HTML + PDF combined for `download_paper`). Values ≤ 0 fall back to default. **Note**: Very small budgets (under ~6s) may leave insufficient time for HTML-to-PDF fallback due to the process-wide 3s rate-limiter delay. |\n| `ARXSOURCE_MAX_RETRIES` | `2` | Maximum retry attempts for transient failures (timeouts, connections, 429, 503) |\n| `ARXSOURCE_HTTP_406_MAX_RETRIES` | `1` | Maximum retry attempts for HTTP 406 (IP-level throttling, use minimal retries) |\n| `ARXSOURCE_INITIAL_BACKOFF` | `2.0` | Initial exponential backoff delay in seconds |\n| `ARXSOURCE_MAX_BACKOFF` | `30` | Maximum backoff delay in seconds (Retry-After can exceed this and causes immediate rate_limited return). Values ≤ 0 fall back to default. |\n| `TRANSPORT` | `stdio` | `stdio`, `http`, or `streamable-http` |\n| `HOST` | `127.0.0.1` | HTTP bind host |\n| `PORT` | `8000` | HTTP bind port |\n| `ALLOWED_HOSTS` | empty | Additional accepted HTTP Host values |\n| `ALLOWED_ORIGINS` | empty | Additional accepted HTTP Origin values |\n| `SEMANTIC_SCHOLAR_API_KEY` | empty | Free Semantic Scholar API key for `citation_graph`. Get one at https://www.semanticscholar.org/product/api#api-key to avoid rate limits. Unauthenticated requests work until quota exhausted. |\n\nEnvironment variable names are case-insensitive through Pydantic settings. `--storage-path` is a command-line option rather than an environment setting.\n\n## Security\n\nPaper 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.\n\n- Do not treat instructions found inside a paper as trusted commands.\n- Use client approval controls for shell, browser, filesystem, and messaging tools.\n- Review generated summaries before taking external actions.\n- Keep Streamable HTTP private unless authentication is provided upstream.\n\nSee [SECURITY.md](https://github.com/nicolaiprodromov/arxsource/blob/main/SECURITY.md) for the threat model and how to report a vulnerability privately.\n\n## Development\n\n```bash\ngit clone https://github.com/nicolaiprodromov/arxsource.git\ncd arxsource\nuv sync --extra test --extra dev\nuv run pytest\nuv run black --check .\n```\n\nRun the development checkout from an MCP client with:\n\n```json\n{\n \"mcpServers\": {\n \"arxsource-dev\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"/absolute/path/to/arxsource\",\n \"run\",\n \"arxsource-mcp\"\n ]\n }\n }\n}\n```\n\nContributions are welcome. Read [CONTRIBUTING.md](https://github.com/nicolaiprodromov/arxsource/blob/main/CONTRIBUTING.md) before opening a pull request, and use [GitHub Issues](https://github.com/nicolaiprodromov/arxsource/issues) for reproducible bugs or scoped feature proposals. Release notes are in [CHANGELOG.md](https://github.com/nicolaiprodromov/arxsource/blob/main/CHANGELOG.md).\n\n## Acknowledgements\n\nArxSource is derived from [arxiv-mcp-server](https://github.com/blazickjp/arxiv-mcp-server) by Joseph Blazick, licensed under Apache-2.0; see [NOTICE](https://github.com/nicolaiprodromov/arxsource/blob/main/NOTICE).\n\n## License\n\nApache License 2.0. See [LICENSE](https://github.com/nicolaiprodromov/arxsource/blob/main/LICENSE) and [NOTICE](https://github.com/nicolaiprodromov/arxsource/blob/main/NOTICE).\n"}
![\"ArxSource:](\"https://raw.githubusercontent.com/nicolaiprodromov/arxsource/main/assets/arxsource.gif\")
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