Paper Search MCP

A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources. The project follows a free-first strategy: prioritize open and public data sources, support optional API keys when they improve stability or coverage, and keep source-specific connectors extensible for advanced users.

Table of Contents

• Overview

• Project Principles

• MCP Authorization Compatibility

• Features

• Source Strategy

• Sci-Hub Notice

• Installation

  • Claude Code (Skill)

  • Method 1 — Smithery

  • Method 2 — uvx

  • Method 3 — uv

  • Method 4 — pip

  • Method 5 — npx

  • Method 6 — Docker

  • Method 7 — Clone & run from source

  • DeepSeek Harness (DSH)

  • Environment Variables

• Contributing

• Demo

• Star History

• License

• TODO

Related MCP server: Academic Paper MCP HTTP/SSE Server

Overview

paper-search-mcp is a Python-based tool for searching and downloading academic papers from various platforms. It provides tools for searching papers, downloading PDFs, and extracting text, making it ideal for researchers and AI-driven workflows. It can be used as an MCP server (for Claude Desktop and other MCP clients) or as a Claude Code skill with a CLI interface.

Project Principles

• Free-First: Public and open sources are the default roadmap. Paid or restricted sources are not the core direction of this project.

• Optional API Keys: API keys are supported only when they improve stability, rate limits, or metadata quality. The MCP should still be usable without them whenever possible.

• LLM-Friendly Retrieval: Search results should be standardized, deduplicated, and as complete as possible for downstream LLM workflows.

• Source Transparency: Different sources have different strengths. The MCP should make those tradeoffs explicit instead of pretending every source supports full-text retrieval.

MCP Authorization Compatibility

The bundled MCP server supports stdio (the default), sse, and streamable-http. Network transports bind to 127.0.0.1 by default. Optional OAuth protected-resource mode adds standard discovery, JWT bearer-token validation and required scopes to both HTTP transports, using an external authorization server.

Enable it with --auth oauth or PAPER_SEARCH_MCP_AUTH=oauth and the explicit issuer/JWKS/resource/audience/scope configuration in OAuth protected-resource setup. Invalid or incomplete HTTP auth settings fail startup. stdio remains independent of HTTP authentication. Local HTTP without OAuth configuration remains open; do not publish that listener directly. A protected remote deployment still needs TLS, rate limits and filesystem isolation. This feature does not host login/accounts or replace the external issuer's OAuth flow; live identity-provider interoperability must be validated for your deployment.

Features

• Two-Layer Architecture:

  • Layer 1 (Unified Tooling): High-level search_papers for multi-source concurrent search & deduplication, and download_with_fallback relying on publisher open access links with sequential fallbacks.

  • Layer 2 (Platform Connectors): Modular connectors for specific academic platforms (arXiv, PubMed, bioRxiv, Semantic Scholar, etc.) equipped with intelligent DOI extraction via regex text analysis or API fields.

• Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, Crossref, OpenAlex, PubMed Central (PMC), CORE, Europe PMC, dblp, OpenAIRE, CiteSeerX, DOAJ, BASE, Zenodo, HAL, SSRN, OpenReview, Unpaywall (DOI lookup), and optional Sci-Hub workflows.

• Opt-in Fast Search: CLI search keeps broad coverage by default. Use -s fast (OpenAlex, Crossref, arXiv, PubMed, Europe PMC) or -s fastest (OpenAlex and Crossref) when lower latency matters more than coverage.

• Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.

• Free-First Design: Open and public sources are prioritized before any optional commercial or restricted integrations.

• Optional API-Key Enhancement: Sources like Semantic Scholar can work better with a user-provided API key, but are not intended to force paid usage.

• Discovery + Retrieval Workflow: Google Scholar and Crossref can be used for discovery and DOI backfilling, while open repositories and publisher links are used for lawful full-text resolution where available.

• OA-First Fallback Chain: download_with_fallback now follows source-native download → OpenAIRE/CORE/Europe PMC/PMC discovery → Unpaywall DOI resolution → optional Sci-Hub.

• Bounded reference lookups: OpenAlex references and citing papers from a DOI or OpenAlex ID, with explicit budgets and truncation metadata.

• MCP Integration: Compatible with MCP clients for LLM context enhancement.

• DeepSeek Harness (DSH) Integration: A dsh profile bundle (clone the repo, npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web add link:./dsh) exposing every tool as mcp__paper-search__*, with an optional guidance skill.

• Extensible Design: Easily add new academic platforms by extending the academic_platforms module.

Source Strategy

The long-term goal is not to depend on a single search engine, but to combine multiple free and public sources with clear roles:

• Open metadata backbone: Crossref, OpenAlex, Semantic Scholar, dblp, CiteSeerX, SSRN, Unpaywall (DOI-centric OA metadata).

• Discipline-specific sources: arXiv, PubMed, PubMed Central, Europe PMC, IACR.

• Open-access full-text sources: arXiv, PMC, CORE, OpenAIRE, DOAJ, BASE, Zenodo, HAL, publisher open-access links.

• Discovery and DOI recovery: Google Scholar can be useful for finding titles, versions, and DOI clues when other public metadata sources are incomplete.

Recommended free-first roadmap:

1. Keep current public sources stable.

2. Add OpenAlex as a broad free metadata source.

3. Add PubMed Central and Europe PMC for stronger biomedical full-text access.

4. Add CORE and OpenAIRE for repository-based open-access retrieval.

5. Use Google Scholar mainly as a discovery fallback, not as the primary canonical source.

Platform Capability Matrix

This matrix reflects verified live-integration results from functional and end-to-end regression tests in this repository. Columns show the highest capability level observed under normal conditions.

✅ = reliable in live tests. ⚠️ = works but subject to upstream instability or access restrictions. ❌ = not supported. 🔑 = key required. 🚧 = skeleton only.

Credential & API Key Requirements

All keys are optional unless noted. Configure them in ~/.config/paper-search-mcp/.env (preferred) or as shell exports.

All variables follow the PAPER_SEARCH_MCP_<NAME> prefix scheme. Legacy names without the prefix (e.g. CORE_API_KEY, UNPAYWALL_EMAIL) are still supported for backward compatibility.

Known Upstream Limitations

Some search failures are caused by external provider instability, not by bugs in this project:

Google Scholar failures are exposed in errors.google_scholar by unified MCP and CLI search, while successful sources still return their papers. Direct Scholar searches raise GoogleScholarSearchError for those failures. A normal empty result page still returns an empty list. If a later page fails, the source is reported as failed rather than returning its earlier pages as complete. Timeouts also raise a source error rather than implying that no papers exist. After exhausted HTTP 403/429/503 retries or a CAPTCHA, the shared Scholar connector enters a 60-second cooldown. Consecutive blocked searches extend it exponentially up to 15 minutes; a valid upstream Retry-After can extend this interval. A successful page resets the streak. Calls during cooldown fail immediately with a retry interval and do not contact Scholar. Retries honor valid Retry-After seconds or HTTP dates up to 24 hours. Longer instructions pause automatic requests for the lifetime of this connector instead of retrying earlier than the provider requested. Delays above 30 seconds are carried across calls without sleeping in a worker. Concurrent calls share the same paced session. The connector does not rotate identity between requests or solve CAPTCHAs. Cooldown state is process-local, not a guarantee of an upstream quota or recovery after that interval.

For a survey, select alternative sources explicitly, for example MCP search_papers(query, sources="google_scholar,openalex,semantic,crossref") or CLI paper-search search "query" -s google_scholar,openalex,semantic,crossref. Successful providers keep their own source labels and their papers even when Scholar is cooling down. Alternatives have their own access/rate limits; they are never silently returned as Google Scholar results.

Local PDF section extraction

extract_sections splits an already-downloaded PDF at recognized standalone headings, preserving extracted text order and page spans with explicit limits and truncation. Reads are confined to the configured download root; no network, OCR, medical inference or new dependency is added. Labels are heuristic and may be uncertain. See bounded PDF sections for access rules, CLI usage and parser limitations.

arXiv PDF downloads and reads

arXiv downloads validate bare modern/legacy paper IDs, serialize and pace requests, check HTTP status, stream at most 100 MiB to a same-directory temporary file, and check the PDF header and parser before atomically replacing the destination. A failed transfer or invalid PDF leaves an existing file unchanged. Downloads use 10-second connect and 30-second read timeouts, not a hard total transfer or parser runtime limit, and do not automatically retry denied responses. read arxiv reports invalid/encrypted/image-only PDFs as errors rather than successful empty output; it does not perform OCR or delete invalid cached files.

Optional Paid Platform Connectors (Phase 3)

IEEE Xplore provides opt-in metadata search, disabled until an existing API key is configured. ACM Digital Library search is keyless and enabled by default, using Crossref metadata restricted to ACM DOI prefix 10.1145.

How to enable:

export PAPER_SEARCH_MCP_IEEE_API_KEY=<your_ieee_key>       # free key at https://developer.ieee.org/

With an IEEE key, ieee and its tools are registered at startup. See IEEE metadata limits and full-text boundaries. ACM (acm, search_acm, download_acm, and read_acm_paper) is always available. Legacy PAPER_SEARCH_MCP_ACM_API_KEY / ACM_API_KEY settings are no longer used and can be removed.

ACM downloads require an ACM DOI such as 10.1145/.... Publisher browser challenges may block scripted access; use download_with_fallback(source="acm", paper_id="10.1145/...", doi="10.1145/...") to try open repositories. read_acm_paper downloads a PDF into save_path before extracting text and can overwrite that file.

Free Source Expansion (Phase 4)

Additional public-source connectors are integrated into the MCP server:

• zenodo: Official Zenodo REST API connector (search + record-dependent PDF/read support).

• hal: HAL public API connector (search + record-dependent PDF/read support).

• ssrn: OpenAlex-backed discovery with strict SSRN identity and validated best-effort public downloads.

• openreview: Anonymous public API v2 papers, excluding reviews/private notes; verified OpenReview-hosted PDF download/read.

• unpaywall: DOI-centric OA metadata source for standalone lookup (search_unpaywall) and fallback URL resolution.

SSRN integration remains compliance-first: it only attempts direct public PDF links exposed by SSRN pages. If login/restricted delivery is required, the connector returns a clear message instead of bypassing access controls.

Sci-Hub Notice

Sci-Hub support can remain available as an optional connector for users who explicitly choose to enable it, but it should not be treated as the default or recommended full-text path.

• download_with_fallback leaves Sci-Hub disabled by default. Pass use_scihub=true only when you explicitly choose to use it.

• Availability is unstable and mirrors change frequently.

• Legal and policy risks vary by jurisdiction.

• README and tool descriptions should clearly state that users are responsible for enabling and using it.

• Open-access and publisher-permitted sources should be tried first whenever possible.

Installation

Choose the method that best fits your workflow. All methods support the same optional API keys.

Claude Code (Skill) — recommended for Claude Code users

Install as a Claude Code skill instead of an MCP server. This gives Claude automatic access to paper search when you mention finding papers, academic literature, etc. — no MCP configuration needed.

Prerequisites: uv and Claude Code.

Step 1 — Install the CLI:

uv tool install paper-search-mcp

Step 2 — Install the skill:

mkdir -p ~/.claude/skills/paper-search
curl -fsSL https://raw.githubusercontent.com/openags/paper-search-mcp/main/claude-code/SKILL.md \
  -o ~/.claude/skills/paper-search/SKILL.md

Step 3 (optional) — Configure API keys:

Create ~/.config/paper-search-mcp/.env for optional API keys (see Environment Variables).

That's it. Next time you start Claude Code, just ask it to find papers — the skill activates automatically. For example:

• "Find me recent papers on CRISPR base editing"

• "Search arxiv and semantic scholar for transformer attention mechanisms"

• "Download the PDF for arxiv paper 2106.12345"

The skill uses a CLI (paper-search) that wraps the same library as the MCP server, outputting JSON for search/download and plain text for read.

paper-search download reports status: "ok" only when the returned path is a readable, non-empty regular file with a PDF header. Connector error strings, missing files, and saved HTML error pages produce a JSON error and exit code 1. This lightweight check does not establish complete PDF integrity or paper identity; those checks remain the responsibility of the source connector and the reader.

Choose sources explicitly when latency matters:

paper-search search "gender imbalance neuroscience references" -s fast -n 3
paper-search search "gender imbalance neuroscience references" -s fastest -n 3
paper-search search "gender imbalance neuroscience references" -s all -n 3
paper-search download semantic DOI:10.1038/s41593-020-0658-y -o ./downloads

The CLI keeps -s all as its default broad source set. --exhaustive is an accepted compatibility no-op because broad search is already the default. Explicit -s selections always take precedence, including -s fast and -s fastest. The broad preset includes the supported public sources; optional paid sources are never added to these presets.

-s fast selects OpenAlex, Crossref, arXiv, PubMed, and Europe PMC. A nonblank PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY (or legacy SEMANTIC_SCHOLAR_API_KEY) also includes Semantic Scholar. -s fastest selects only OpenAlex and Crossref. Presets are latency-oriented choices, not guarantees of response time or exhaustive literature coverage. Anonymous Semantic Scholar 429 responses fail immediately; authenticated requests retain bounded retries.

Only selected searchers are constructed. Source lists and presets are honored exactly: a DOI in the query never adds another source. Use -s unpaywall for a DOI lookup, or include unpaywall explicitly in a comma-separated source list. The broad all preset already includes it. MCP server search defaults are unchanged.

To stop a slow source from holding up a CLI search, opt into a per-source deadline:

paper-search search "robot scientist" -s fast --source-timeout 45

--source-timeout SECONDS must be positive and finite. It runs selected sources in separate Python processes, at most four at once. Each source's budget starts after OS process creation returns and includes Python/provider initialization, search, serialization, and worker exit. Process creation, time waiting for a worker slot, and process cleanup add overhead outside that budget. It is not a deadline for the whole command. Timed-out workers are killed and reaped before their slots are reused, so a blocked network call cannot hold up Python's thread-executor shutdown. Successful sources keep their results and timed-out sources appear in errors with a zero source_results count. Source selection, labels, sorting, JSON fields, and partial-result exit status (0) are unchanged. Subprocess startup adds overhead, especially for very small budgets. Without the flag, existing connector-specific timeouts and in-process search are unchanged.

Sort search results by citation count or publication date:

paper-search search "transformer attention" --sources arxiv,semantic --sort citations
paper-search search "CRISPR" --sort date

--sort relevance (the default) preserves the existing order: selected sources in order, with each source's returned order unchanged. It does not compute a cross-source relevance score. --sort citations orders highest counts first; --sort date orders newest publication dates first. Sorting is client-side, after deduplication, and only covers retrieved results (--max-results is per source); it does not change source queries or search the full source collection for its most-cited/newest papers. Ties retain their original order. Missing or invalid values sort last; numeric citation strings are supported. Dates accept ISO dates/timestamps, with naive timestamps and date-only values treated as UTC. The default JSON output and selected sources are unchanged.

MCP tools from the CLI

paper-search tool calls the same registered tools as the MCP server, including source-specific options and DOI lookup. Required arguments are positional; optional arguments use kebab-case flags. Boolean flags use --flag and --no-flag; omitted options retain their MCP defaults.

paper-search tool --help
paper-search tool --list  # JSON tool list, schemas, and annotations
paper-search tool search_crossref --help
paper-search tool search_crossref "transformer attention" --filter from-pub-date:2024-01-01 --sort published --order desc --max-results 2
paper-search tool get_crossref_paper_by_doi 10.1038/nature12373
paper-search tool search_iacr "cryptography" --no-fetch-details

Objects and lists return JSON; text and download paths return plain text. An exception returns a JSON error with exit code 1; invalid CLI syntax exits with code 2. A normal MCP result retains its original meaning, including error or partial-result fields returned by the tool itself. --list shows only tools enabled by the current configuration, so IEEE tools still require their API key. The existing search, download, read, and sources commands are unchanged. Tool calls keep the MCP tools' source selection and timeout behavior; Sci-Hub fallback remains opt-in through --use-scihub.

Skill ZIP uploads and other Claude runtimes

The steps above install a local Claude Code skill at ~/.claude/skills/paper-search/SKILL.md, following the Claude Code skill layout. They do not require a ZIP upload.

GitHub's Download ZIP contains the entire repository, with the skill nested under paper-search-mcp-main/claude-code/. It is not a standalone skill archive. If an uploader reports that SKILL.md is nested too deeply, check the archive layout first. Anthropic's custom-skill packaging guide specifies a single skill folder at the archive root, with the skill file directly inside it:

paper-search-skill.zip
└── paper-search/
    └── SKILL.md

To create that layout for inspection or adaptation, run this from a repository checkout (requires Python 3; overwrites paper-search-skill.zip):

from zipfile import ZIP_DEFLATED, ZipFile

with ZipFile("paper-search-skill.zip", "w", compression=ZIP_DEFLATED) as archive:
    archive.write("claude-code/SKILL.md", arcname="paper-search/SKILL.md")

A reusable builder is also available: python scripts/build_skill_zip.py --output paper-search-skill.zip. It refuses to overwrite an existing file unless --force is supplied. Its archive layout, frontmatter, and byte-for-byte instruction contents are covered by offline tests.

This only packages the instructions; it does not bundle Python dependencies, install paper-search, or configure an MCP connection. The bundled skill expects a runtime that can execute the CLI and reach the academic services. Upload acceptance and execution in Claude web or another runtime have not been validated by this project. Check that runtime's current metadata, package-installation, network-access, and code-execution requirements before adapting the skill. If you want to use an MCP client instead, follow the MCP installation methods below; uploading a skill ZIP does not start or connect an external MCP server.

MCP Server Config file locations (for methods below)

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

Method 1 — Smithery (one-command, recommended for Claude Desktop)

npx -y @smithery/cli install @openags/paper-search-mcp --client claude

Smithery automatically writes the correct config block for you. No manual JSON editing needed.

Method 2 — uvx (no install, always latest)

uvx runs the package directly from PyPI without a permanent install. Requires uv.

# Install uv (skip if already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

⚠️ macOS note: uvx generated wrapper scripts rely on realpath, which is not included in macOS by default. If you see a realpath: command not found error, either install GNU coreutils (brew install coreutils) or use Method 3 (uv run) instead — it does not have this limitation.

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uvx",
      "args": ["paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": ""
      }
    }
  }
}

Method 3 — uv (persistent install)

uv tool install paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uv",
      "args": ["tool", "run", "paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": ""
      }
    }
  }
}

Method 4 — pip (standard Python install)

pip install paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "python",
      "args": ["-m", "paper_search_mcp.server"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": ""
      }
    }
  }
}

If python is not on your PATH, replace it with the full path (e.g. /usr/bin/python3 or C:\Python311\python.exe). Run which python3 / where python to find it.

Method 5 — npx (via Smithery CLI, no local Python needed)

npx -y @smithery/cli run @openags/paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "npx",
      "args": ["-y", "@smithery/cli", "run", "@openags/paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": ""
      }
    }
  }
}

Method 6 — Docker

docker build -t paper-search-mcp .
docker run --rm -i \
  -e PAPER_SEARCH_MCP_UNPAYWALL_EMAIL=your@email.com \
  -e PAPER_SEARCH_MCP_CORE_API_KEY=your_core_key \
  paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": ""
      }
    }
  }
}

Method 7 — Clone & run from source (development / recommended for macOS local)

This is the most reliable method on macOS — no wrapper scripts, no realpath issues.

# 1. Install uv (skip if already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Clone repo
git clone https://github.com/openags/paper-search-mcp.git
cd paper-search-mcp

# 3. Verify it runs (uv auto-resolves dependencies, no manual install needed)
uv run -m paper_search_mcp.server

Claude Desktop config (replace the directory path with your actual clone location):

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/path/to/paper-search-mcp",
        "-m", "paper_search_mcp.server"
      ],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": ""
      }
    }
  }
}

For example, if you cloned to /Users/mac/Pengsong/paper-search-mcp:

"args": ["run", "--directory", "/Users/mac/Pengsong/paper-search-mcp", "-m", "paper_search_mcp.server"]

uv run automatically installs dependencies into an isolated environment on first run — no pip install or venv needed.

To run one shared network server instead of one stdio process per client:

paper-search-mcp --transport streamable-http --host 127.0.0.1 --port 8000 --path /mcp

The available transports are stdio, sse, and streamable-http. The default remains stdio. The same network settings can be supplied with PAPER_SEARCH_MCP_TRANSPORT, PAPER_SEARCH_MCP_HOST, PAPER_SEARCH_MCP_PORT, and PAPER_SEARCH_MCP_PATH; command-line options take precedence. With the default --auth none, binding to a non-loopback host such as 0.0.0.0 exposes an unauthenticated server. For protected HTTP, configure optional OAuth mode and keep the backend behind TLS and appropriate deployment controls; otherwise use an authenticated gateway.

For active development, optionally install an editable copy:

uv venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"

DeepSeek Harness (DSH)

Paper search is available inside DeepSeek Harness (dsh) as a profile bundle: it boots the MCP server and registers all its tools in any dsh profile as mcp__paper-search__*. The bundle only mounts a composition row — the MCP server itself is untouched.

Prerequisites: uv (the default launcher is uvx) and pnpm (dsh plugin forwards to pnpm). Commands below use the npx @deepseek-ai/dsh launcher (no global install needed); with a global dsh install, drop the prefix.

git clone https://github.com/openags/paper-search-mcp.git
cd paper-search-mcp
npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web add link:./dsh

link: updates the bundle configuration from the checkout. git pull does not update the separately launched PyPI server; see dsh/README.md for a source-checkout launcher and reproducible version selection. Replace web with your profile name; a missing profile is initialized automatically. Restart the profile (npx @deepseek-ai/dsh@0.2.0-rc.2 web, or relaunch) and the tools appear — e.g. mcp__paper-search__search_papers, mcp__paper-search__download_with_fallback, mcp__paper-search__search_arxiv.

API keys: just follow Environment Variables — the server auto-loads ~/.config/paper-search-mcp/.env. DSH deliberately scrubs credential-shaped ambient env vars from spawned processes, so shell exports do not reach the server; to forward variables explicitly, override the mcp-paper-search row in ~/.dsh/profiles/<name>/cordis.patch.yml (a config override replaces the whole object — see dsh/README.md for a complete example).

Optional skill with usage guidance (workflow, source table, tool mapping):

mkdir -p ~/.dsh/skills && cp -r dsh/skills/paper-search ~/.dsh/skills/

Uninstall:

npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web remove paper-search-mcp-dsh
rm -rf ~/.dsh/skills/paper-search

Removing the bundle reconciles its row out of the composition. It does not delete downloaded papers, package caches, or user-managed settings.

If you already run paper-search-mcp through your own @deepseek-ai/dsh-mcp-client row, remove that row (or give one of the two a distinct serverName) before adding the bundle — duplicate serverNames fail at load. The bundle package version tracks this checkout's Python project version; dsh/README.md documents alternate launchers (uv tool run, python -m, npx), environment forwarding, and development.

Environment Variables (.env file)

Instead of putting keys directly in the JSON config you can store them in the user config file (auto-loaded on startup):

mkdir -p ~/.config/paper-search-mcp
curl -fsSL https://raw.githubusercontent.com/openags/paper-search-mcp/main/.env.example \
  -o ~/.config/paper-search-mcp/.env
$EDITOR ~/.config/paper-search-mcp/.env

PAPER_SEARCH_MCP_UNPAYWALL_EMAIL=your@email.com
PAPER_SEARCH_MCP_CORE_API_KEY=
PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY=
PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN=
PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL=
PAPER_SEARCH_MCP_IEEE_API_KEY=

To use a custom path: export PAPER_SEARCH_MCP_ENV_FILE=/absolute/path/to/.env

Legacy variable names without the PAPER_SEARCH_MCP_ prefix (e.g. CORE_API_KEY, UNPAYWALL_EMAIL) are still supported for backward compatibility.

Optional local search cache

Opt-in SQLite search caching adds TTL, size limits, status, and clearing. It is disabled by default and bypasses authenticated sources.

Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository: Click "Fork" on GitHub.

2. Clone and Set Up:

   git clone https://github.com/yourusername/paper-search-mcp.git
   cd paper-search-mcp
   uv venv && source .venv/bin/activate
   uv pip install -e ".[dev]"

3. Make Changes:

   • Add new platforms in academic_platforms/.

   • Update tests in tests/.

4. Submit a Pull Request: Push changes and create a PR on GitHub.

Demo

TODO

Planned Academic Platforms

• [√] arXiv

• [√] PubMed

• [√] bioRxiv

• [√] medRxiv

• [√] Google Scholar

• [√] IACR ePrint Archive

• [√] Semantic Scholar

• [√] Crossref

• [√] PubMed Central (PMC)

• [√] CORE

• [√] Europe PMC

• [√] Sci-Hub warning and enablement docs

Development Tasks

• [√] Fix Async search bugs and ensure reliable fast MCP events

• [√] End-to-End full pipeline testing script (search, parse, download)

• [√] Establish two-layer federated architecture (Layer 1 tool: search_papers)

• [√] Ensure pervasive DOI extraction across metadata fields & abstract fallbacks

• Citation graph & Paper relation context feature

• [√] Expand full-stack OpenAlex provider

Priority Free and Open Sources

• [√] PubMed Central (PMC)

• [√] CORE

• [√] OpenAlex

• [√] Europe PMC

• [√] OpenAIRE

• [√] dblp

• [√] CiteSeerX

• [√] DOAJ

• [√] BASE

• [√] Zenodo

• [√] HAL

• [√] SSRN (discovery + best-effort full-text)

• [√] Unpaywall (standalone DOI search source)

Optional and Non-Core Integrations

• ResearchGate

• JSTOR

• ScienceDirect

• Springer Link

• [√] IEEE Xplore (optional skeleton — activate with IEEE_API_KEY)

• [√] ACM Digital Library (keyless Crossref search; publisher PDF access varies)

• Web of Science

• Scopus

Star History

License

This project is licensed under the MIT License. See the LICENSE file for details.

Happy researching with paper-search-mcp! If you encounter issues, open a GitHub issue.

Explicit institutional metadata connectors

Web of Science Starter is available only through search_wos or an explicit wos source (paper-search search 'TI=(machine learning)' -s wos). Supplying a key does not add it to default, all, fast, or fastest searches. Supply PAPER_SEARCH_MCP_WOS_API_KEY (legacy WOS_API_KEY also works) at runtime; the connector does not create accounts or persist credentials. See the institutional connector guide for limits, errors, capability boundaries, and validation status.

Scopus is likewise explicit-only (-s scopus or MCP search_scopus), using PAPER_SEARCH_MCP_SCOPUS_API_KEY (legacy SCOPUS_API_KEY). Metadata search defaults to STANDARD. Abstract retrieval is available via paper-search read scopus ID or MCP read_scopus_paper; ScienceDirect article retrieval requires explicit --full-text / full_text=true and a verified DOI/PII match. The response labels full_text, abstract_only, and unavailable separately. Neither connector's live institutional entitlement has been validated by these fixture tests.