Semantic Scholar MCP Server
PYPI · S2-MCP-SERVER
MCP server for the Semantic Scholar API: search 200M+ papers, citations, authors, recommendations.
Available components
Last scanned (2 hours ago)
Scores higher than 73% of PyPI servers
Category Education and Research
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 0 of 28 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency100
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to smaniches/semantic-scholar-mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 0 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability60
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1727 tokens (~123/item across 14 items; 14 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management97
- Stability observed for 29 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage67
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 14 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 15 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Checks we ran (15)
- Known CVEs
- Install scripts
- Dependency health
- Source repository
- Provenance
- License
- Maintenance
- Security disclosure
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Checks we could not complete (1)
These are gaps on our side, not findings about this package. We only credit what we can confirm, so a category made up entirely of them scores 0.
- Malware scan in Supply Chain SecurityMalware scan not yet available for this package.
Does not apply to this package (2)
- Endpoint SecurityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
- Transport & ReachabilityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
The score does not measure everything. What the score does not measure →
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +1
- Malware scan: unverified → pass ▲ security
- Package version: 1.7.14 → 1.7.15 functional
- 4 Nov 26 −16
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.93 functional
- Package version: 1.7.13 → 1.7.14 functional
- 31 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- Package version: 1.7.8 → 1.7.13 functional
- 30 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 1.7.12 → 1.7.13 functional
- 28 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 27 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 1.7.11 → 1.7.12 functional
- 26 Oct 26 0
- Stability: 0.97 → pass security
- 25 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/s2-mcp-server@1.7.15
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | smaniches/semantic-scholar-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/smaniches/semantic-scholar-mcp/.github/workflows/publish.yml@refs/tags/semantic-scholar-mcp-v1.7.15 |
| Rekor log index | 3696601472 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:d091d897a58622541af85bbb4aade41db7408c510074b198f88d515d0be5095d |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | Known build tool | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
semantic_scholar_author_batch ~30
Retrieve multiple authors in a single request (max 1000).
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_bulk_papers ~30
Retrieve multiple papers in a single request (max 500).
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_bulk_search ~92
Search papers with sorting and cursor-based pagination for large result sets. Unlike regular search, supports sorting (e.g., by citation count), boolean query syntax, and returns a continuation token for paging through all results. The observed endpoint ignores ``limit`` and may return up to 1000 papers per page; the full page is returned so no row is skipped.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_export_citation ~118
Export a citation for a paper in BibTeX format. Use once you have a paper ID (from semantic_scholar_search_papers or semantic_scholar_match_paper), e.g. paper_id='DOI:10.18653/v1/N18-3011'. Returns the BibTeX entry as plain text - there is no response_format option. Raises an error for an unknown ID, a paper without citation data, or any format other than 'bibtex'.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_get_author ~25
Get author profile with optional publications list.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_get_paper ~247
Get paper details. Accepts: S2 ID, DOI:xxx, ARXIV:xxx, PMID:xxx, CorpusId:xxx Returns title, abstract, authors, venue, year, citation counts, TLDR, and open-access PDF link for one paper, e.g. paper_id='ARXIV:1706.03762'. Set include_citations / include_references to also list citing and referenced papers (fetched in parallel, 1-100 per page each). Paginate the two directions independently with citations_offset/references_offset; JSON output exposes citations_page.next and references_page.next. Results are cached in memory for 5 minutes; an unknown ID raises a not-found error. Client-side requests use a 1.0-second unkeyed interval and a conservative 1.1-second authenticated default; explicitly higher S2-assigned quotas can be configured. 429/502/503 responses retry automatically with backoff. Returns Markdown by default, response_format='json' for raw JSON. To fetch many papers at once use semantic_scholar_bulk_papers.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_match_paper ~34
Find the closest title match; preserve S2's unscaled numeric matchScore.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_multi_recommend ~175
Get recommendations using multiple positive and negative example papers. Use instead of semantic_scholar_recommendations when steering with more than one example: results resemble positive_paper_ids and are pushed away from negative_paper_ids. Example: positive_paper_ids=['ARXIV:1706.03762', 'DOI:10.18653/v1/N19-1423'], negative_paper_ids=['ARXIV:1409.0473']. Accepts 1-100 positive and up to 100 negative IDs in any supported paper-ID format; malformed IDs raise an error before any request is made. Returns up to `limit` (1-500, default 10) papers, Markdown by default or response_format='json' for raw JSON.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_paper_authors ~129
Get full author profiles for a paper's authors. Unlike the abbreviated author list embedded in semantic_scholar_get_paper results, this returns each author's complete profile - affiliations, h-index, paper and citation counts - plus author IDs usable with semantic_scholar_get_author. Example: paper_id='DOI:10.18653/v1/N18-3011'. Authors are returned in listed order (limit 1-1000, default 100). Returns Markdown by default, response_format='json' for raw JSON.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_recommendations ~192
Get paper recommendations based on a seed paper. Provide one paper you already know (e.g. paper_id='ARXIV:1706.03762') and receive up to `limit` similar papers. from_pool picks the candidate pool: 'recent' (default, recently published papers from all fields) or 'all-cs' (computer-science papers of any age). When steering with several positive or negative examples, use semantic_scholar_multi_recommend instead. An unknown seed ID raises a not-found error. Client-side requests use a 1.0-second unkeyed interval and a conservative 1.1-second authenticated default; explicitly higher S2-assigned quotas can be configured. 429/502/503 responses retry automatically with backoff. Returns Markdown by default, response_format='json' for raw JSON.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_search_authors ~120
Search for academic authors by name. Example: query='Yoshua Bengio'. Several distinct researchers can share a name, so confirm identity with semantic_scholar_get_author (affiliations, h-index, publications) before attributing work; to list the authors of a specific paper use semantic_scholar_paper_authors instead. Page with offset/limit (max 100 per call, default 10). Returns Markdown by default, response_format='json' for raw JSON.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_search_papers ~153
Search for academic papers. Relevance-ranked plain-text search over 200M+ papers, with year, field-of-study, publication-type, open-access, and citation-count filters. The query is sent unchanged; boolean and quoted-phrase syntax is documented only for semantic_scholar_bulk_search. Page with offset/limit (max 100 per call). For sorted or very large result sets use semantic_scholar_bulk_search; to search inside paper full text use semantic_scholar_snippet_search; to resolve one known title use semantic_scholar_match_paper. Returns Markdown by default, response_format='json' for raw JSON.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_snippet_search ~44
Search within paper full text. Returns text snippets with context. Note: This endpoint is heavily rate-limited without an API key.
| Name | Type | Req | Description |
|---|---|---|---|
| params | – | yes | – |
No output schema declared.
No examples provided.
semantic_scholar_status ~22
Check server health, API connectivity, and key status.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
How do I install the Semantic Scholar MCP Server server?
Semantic Scholar MCP Server runs locally as a PyPI package, launched with uvx s2-mcp-server. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · s2-mcp-server
claude mcp add smaniches-semantic-scholar-mcp -- uvx s2-mcp-server
What is the Semantic Scholar MCP Server server?
Semantic Scholar MCP Server is listed in the public MCP registry as io.github.smaniches/semantic-scholar-mcp. MCP server for the Semantic Scholar API: search 200M+ papers, citations, authors, recommendations. This page covers its PyPI package (s2-mcp-server).
Is the Semantic Scholar MCP Server server safe to use?
Semantic Scholar MCP Server scores 75 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Semantic Scholar MCP Server server expose?
Semantic Scholar MCP Server exposes 14 tools: semantic_scholar_search_papers, semantic_scholar_get_paper, semantic_scholar_search_authors, semantic_scholar_get_author, semantic_scholar_recommendations, and 9 more. Their descriptions and schemas cost roughly 1,411 tokens of context every time the server is loaded.
Is the Semantic Scholar MCP Server server still maintained?
Semantic Scholar MCP Server is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Semantic Scholar MCP Server server under?
Semantic Scholar MCP Server declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.
What did VerifyMCP test on the Semantic Scholar MCP Server server?
VerifyMCP ran 15 checks on Semantic Scholar MCP Server, covering Known CVEs, Install scripts, Dependency health, Source repository and 11 more. One item could not be completed, a gap on our side rather than a finding about Semantic Scholar MCP Server: Malware scan. Endpoint Security and Transport & Reachability do not apply to a PyPI package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.