Scholar MCP

Scholar MCP

Scholarly papers, patents, books, standards and PDF conversion

Documentation | Config wizard | PyPI | Docker

Features

29 tools, organised by scholarly source type.

Source domains

• Papers: full-text search with year/venue/field/citation filters; single-paper lookup by DOI, S2 ID, arXiv ID, ACM ID, or PubMed ID; author profile and name search; forward citations, backward references, BFS graph traversal, shortest-path bridge discovery; recommendations from positive/negative examples; BibTeX/CSL-JSON/RIS citation generation with OpenAlex venue enrichment.
• Patents: search across 100+ patent offices via EPO OPS with CPC/applicant/inventor/jurisdiction filters; bibliographic, claims, description, family, legal, and citations sections; NPL-to-paper resolution via Semantic Scholar and paper-to-patent citation discovery. EPO credentials are optional; other domains work without them.
• Books: Open Library search by title/author/keywords, no API key required; lookup by ISBN-10/13 or by Open Library work/edition ID; subject-based recommendations sorted by popularity; Google Books excerpts and preview links; WorldCat permalinks for library discovery; cover image caching. Papers with an ISBN in externalIds are automatically enriched with publisher, edition, cover URL, and subject data from Open Library.
• Standards: identifier resolution, search, and metadata retrieval for NIST, IETF, W3C, and ETSI standards, with optional full-text fetch and Markdown conversion via docling. Tier 2 ISO, IEC, IEEE, Common Criteria (CC), and CEN/CENELEC metadata (including ISO/IEC/IEEE joint standards and the CC ↔ ISO/IEC 15408 cross-link) is synced locally via sync-standards. ISO, IEC, IEEE have a live-fetch fallback for unsynced identifiers; CC and CEN have no live API and require a sync first. Citations matching standards patterns (RFC, ISO, NIST SP, IEEE, EN, CC) are automatically enriched with structured standard_metadata including identifier, title, body, status, and full-text URL when available (see docs/guides/standards.md).

Cross-cutting

• Enrichment pipeline: phased enrichment from multiple sources: OpenAlex (OA status, affiliations, funders, concepts), CrossRef (publisher, page ranges, container titles), Google Books (preview links, excerpts), and Open Library (book metadata). Runs automatically on paper and book results.
• PDF conversion: download open-access PDFs and convert to Markdown via docling-serve, with optional VLM enrichment for formulas and figures; automatic fallback to ArXiv, PubMed Central, and Unpaywall when Semantic Scholar has no OA link; direct URL download for PDFs found elsewhere; converted text is paged so large documents fit in MCP responses.
• Intelligent caching: SQLite-backed cache with per-table TTLs (30 days for papers/authors, 7 days for citations/references) and identifier aliasing.
• Authentication: bearer token, OIDC (OAuth 2.1), or both simultaneously (multi-auth).
• Multi-transport: stdio (Claude Desktop), HTTP (streamable-http), and SSE transports.
• Linux packages: .deb and .rpm packages with systemd service and security hardening.

Coverage by domain

Per-domain depth is uneven. Papers currently have the richest tool surface (citation graph, recommendations, cross-referencing to all three other domains); standards are the leanest. That reflects public data availability, not a value hierarchy: writing a paper typically needs all four source types for citations and prior art. Parity work is tracked in GitHub issues and milestones; the roadmap shows intent, not a completeness commitment.

What you can do with it

With this server mounted in an MCP client (Claude, etc.), you can:

• Survey a field: "Find the 20 most-cited papers on graph neural networks from 2020 to 2024 and draft a literature review outline." Composes search_papers + get_citations + enrich_paper.
• Trace a citation path: "What's the shortest citation path from 'Attention is All You Need' to 'RLHF for dialogue agents'?" Uses find_bridge_papers + get_citation_graph.
• Cross-reference prior art: "For this patent family, list academic papers it cites and any books or standards that show up in the description." Composes get_patent + batch_resolve + standards/book enrichment.
• Generate a bibliography: "Emit BibTeX for these 30 DOIs with OpenAlex venue data." Uses generate_citations.
• Look up a standard: "What's the latest status of RFC 9000, and fetch the Markdown full text." Uses resolve_standard_identifier + get_standard.

Installation

From PyPI

pip install pvliesdonk-scholar-mcp

If you add optional extras via the PROJECT-EXTRAS-START / PROJECT-EXTRAS-END sentinels in pyproject.toml, document them below:

• [all]: every production feature in one extra; the Linux packages and the Claude Desktop bundle install it.
• [debug]: adds the remote debugger (debugpy); see Remote debugging.

The base package already carries FastMCP through fastmcp-pvl-core, so pip install pvliesdonk-scholar-mcp is enough to run scholar-mcp serve. The older [mcp] extra is kept so existing install commands keep working; it adds nothing beyond the base package.

From source

git clone https://github.com/pvliesdonk/scholar-mcp.git
cd scholar-mcp
uv sync --all-extras --all-groups

Docker

docker pull ghcr.io/pvliesdonk/scholar-mcp:latest

To run the newest merged code instead of the newest release, use the rolling edge tag. It is rebuilt on every merge to main and carries no version identity. See Image tags for the full tag list.

docker pull ghcr.io/pvliesdonk/scholar-mcp:edge

A compose.yml ships at the repo root and runs as-is: copy .env.example to .env, then docker compose up -d. It publishes port 8000 on the host and assumes no reverse proxy; Docker Compose covers the configuration split, the domain sentinel blocks, and a Traefik overlay.

To attach a remote Python debugger (development only; the protocol is unauthenticated), see Remote debugging.

Linux packages (.deb / .rpm)

Download .deb or .rpm packages from the GitHub Releases page. Both install a hardened systemd unit; env configuration is sourced from /etc/scholar-mcp/env (copy from the shipped /etc/scholar-mcp/env.example).

Claude Desktop (.mcpb bundle)

Download the .mcpb bundle from the GitHub Releases page and double-click to install, or run:

mcpb install scholar-mcp-<version>.mcpb

Claude Desktop prompts for required env vars via a GUI wizard, with no manual JSON editing needed.

For manual Claude Desktop configuration and setup options, see Claude Desktop deployment.

Release channels

Artifacts ship on three channels. Each row lists exactly what that channel publishes.

Pre-releases reach PyPI so that a candidate's .mcpb bundle installs: the bundle points at PyPI rather than carrying the code. Ordinary installers never see them, because a PEP 440 resolver skips pre-releases unless the requirement pins one or you pass --pre. Ask for a candidate by name with pip install pvliesdonk-scholar-mcp==X.Y.ZrcN. PyPI spells it in the PEP 440 canonical form, while tags use SemVer. Rolling pointers are ordering-aware, so a patch release cut from an old release/X.Y branch never moves latest-style tags back to older content, and a candidate for an already-released version never moves rc. See Release process for the full model.

Quick start

scholar-mcp serve                                # stdio transport
scholar-mcp serve --transport http --port 8000   # streamable HTTP

For library usage (embedding the domain logic without the MCP transport), import from the scholar_mcp package directly. See the project's domain modules under src/scholar_mcp/ for entry points.

Server info

The server registers a built-in get_server_info tool (via fastmcp_pvl_core.register_server_info_tool) so operators can confirm the deployed version with a single MCP call. The default response carries server_name, server_version, and core_version. Servers that talk to a remote upstream wire upstream version reporting inside the DOMAIN-UPSTREAM-START / DOMAIN-UPSTREAM-END sentinel in src/scholar_mcp/server.py; see tool-registration for the wiring pattern.

Health

The server serves /health (liveness, a static 200) and /health/ready (readiness, 503 when a backing store or a domain check fails) outside the MCP mount and outside auth, via fastmcp_pvl_core.register_health_routes. compose.yml probes the first. Domain readiness checks go in the health_checks dict in src/scholar_mcp/server.py; see Docker deployment for the routes, the mount-path rule, and SCHOLAR_MCP_HEALTH_DETAIL.

Configuration

The most common environment variables, shared across all fastmcp-pvl-core-based services:

This table and the one under Domain configuration are curated subsets. The complete generated reference, with every variable the server reads, is the configuration reference; .env.example lists the same surface in copy-paste form.

Authentication

Callers authenticate via a bearer token or OIDC (mutually exclusive). See the Authentication guide for setup, mapped multi-subject tokens, OIDC, and troubleshooting.

Post-scaffold checklist

After copier copy and gh repo create --push:

1. Fill in the DOMAIN blocks (every section marked with a DOMAIN sentinel comment) in this README and in AGENTS.md. The GENERATED-ENV-TABLE-* regions are not DOMAIN blocks; the config generator owns them and rewrites them on every run.
2. Configure GitHub secrets (see below).
3. Install dev + docs tooling: uv sync --all-extras --all-groups.
4. Install pre-commit hooks: uv run pre-commit install.
5. Run the gate locally: uv run pytest -x -q && uv run ruff check --fix . && uv run ruff format . && uv run mypy src/ tests/.
6. Push the first commit. CI should be green.

GitHub secrets

CI workflows reference two required repository secrets and one optional Claude token. Configure them via Settings → Secrets and variables → Actions or with gh secret set:

gh secret set RELEASE_TOKEN
gh secret set SONAR_TOKEN
# Optional: enables @claude and opted-in automatic review.
gh secret set CLAUDE_CODE_OAUTH_TOKEN

Dependency updates are handled by Renovate (renovate.yml), which reuses RELEASE_TOKEN. It maintains uv.lock and auto-merges patch/minor bumps once the CI Success check is green; bootstrap.yml enables auto-merge, applies the repository rulesets (.github/rulesets/), turns on private vulnerability reporting and Dependabot alerts, and fills the repository's About block (description, website, topics) from pyproject.toml on first push. See Repository Protection for the per-branch posture, bypass model, and security settings. GitHub Actions are updated in the copier template and arrive via copier update, not per-repo.

GITHUB_TOKEN is auto-provided; no action needed.

Local development

The PR gate (matches CI):

uv run pytest -x -q                                  # tests
uv run ruff check --fix . && uv run ruff format .    # lint + format
uv run mypy src/ tests/                              # type-check

Pre-commit runs a subset of the gate on each commit; see .pre-commit-config.yaml for details, or AGENTS.md for the full Hard PR Acceptance Gates.

CI requires tests to pass on Python 3.11 through 3.14. Python 3.14 also collects branch coverage and enforces the 80% total and patch coverage thresholds. To reproduce that test command, run uv run --python 3.14 pytest --cov --cov-report=xml --durations=20.

Troubleshooting

Moving a scaffolded project

uv sync creates .venv/bin/* scripts with absolute shebangs pointing at the venv Python. If you move the repo after scaffolding (mv /old/path /new/path), uv run pytest fails with ModuleNotFoundError: No module named 'fastmcp' because the stale shebang resolves to a different interpreter than the venv's site-packages.

Fix:

rm -rf .venv
uv sync --all-extras --all-groups

uv run python -m pytest also works as a one-shot workaround (bypasses the stale entry-script shim).

uv.lock refresh after copier update

When copier update introduces new dependencies (such as a new extra added to pyproject.toml.jinja), the CI install step runs uv sync --locked, which fails against a stale lockfile. Run uv lock locally and commit the refreshed uv.lock alongside accepting the copier-update PR.

CI installs with --locked (and the review workflow with --frozen) so no job ever rewrites uv.lock in its own workspace: a job that re-locks hides the drift it just repaired, and a dirty workspace breaks any later git checkout in the same job. Lockfile drift then shows up as a red install step with a clear message, not as a silent mutation.

Contributing

CONTRIBUTING.md holds the rules for issues and pull requests, and where a fix belongs: fastmcp-pvl-core for library code, the template for template-owned files, this repository for anything inside its DOMAIN-* / CONFIG-* / PROJECT-* blocks. AGENTS.md carries the conventions and gates; the skills under .agents/skills/ carry the task procedures, among them self-reviewing (local self-review before a pull request), writing-release-notes (release notes), applying-template-updates (the weekly template update pull request) and authoring-issues-prs (filing). The release procedure is in docs/deployment/release-process.md; the template update procedure in docs/deployment/template-updates.md. SECURITY.md says how to report a vulnerability privately, and what to expect after.

Links

• Documentation
• llms.txt
• FastMCP
• fastmcp-pvl-core

Domain configuration

The variables this project features as its entry points (domain variables use the SCHOLAR_MCP_ prefix):

This is a curated subset: a field appears here when its tags metadata includes readme. Every domain variable is documented in the configuration reference, grouped the same way the config wizard presents them.

Domain-config fields are composed inside src/scholar_mcp/config.py between the CONFIG-FIELDS-START / CONFIG-FIELDS-END sentinels; env reads go through fastmcp_pvl_core.env(_ENV_PREFIX, "SUFFIX", default) so naming stays consistent, and field invariants go in __post_init__ between the CONFIG-VALIDATE-START / CONFIG-VALIDATE-END sentinels. Each field's metadata help, tags, and wizard group generate the reference tables directly, so keep them accurate and complete.

Key design decisions

• Library-first, MCP-optional. The core domain logic (S2/EPO/Open Library/standards clients, enrichment pipeline, cache) is importable without FastMCP; the MCP server is a thin async wrapper. Enables reuse in scripts, notebooks, and other servers.
• Sync domain code, async MCP layer. Backend clients are synchronous; MCP tools call them via asyncio.to_thread(). Simpler client code, explicit offloading at the transport boundary.
• SQLite cache with per-table TTLs and identifier aliases. Papers / authors last 30 days, citations / references 7 days. DOI ↔ S2 ID ↔ arXiv ID aliasing survives across cache clears so repeated enrichment hits the same row.
• Read-only by default. Write-tagged tools (PDF download/convert, patent PDF) are hidden unless SCHOLAR_MCP_READ_ONLY=false. Safer default for first-run.
• Slow work becomes a background job. Every tool whose work can run long uses the fastmcp-pvl-core jobs layer. A call that beats SCHOLAR_MCP_JOBS_SOFT_DEADLINE_S returns its result directly; a slower one returns a handle to poll with get_job_result. S2-backed tools also return a reasoned handle as soon as Semantic Scholar throttles them. The work continues from where it paused, and a cache hit still answers directly.
• EPO throttling is waited out, not queued. The traffic light is consulted before every request and cached for a minute, so a retry sooner than that would re-read the cache rather than ask again. Each backoff outlasts the cache; an exhausted daily quota is reported immediately instead, since it will not clear today.
• Tier 2 standards sync out-of-band. ISO/IEC/IEEE/CC/CEN catalogues come from community Relaton dumps via scholar-mcp sync-standards, not live at runtime, which avoids paywalled-HTML scraping and keeps tool calls fast.