Scholar RAG MCP
PYPI · SCHOLAR-RAG-MCP · SCANNED SEP 20
Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.
Available components
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 native-build step with no shell scripting around it. View diagnostics → Pass
- 4 of 48 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency32
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: no license is declared. See how to fix → Fail
- Actively maintained (last published 16 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability59
- AI-judged instruction clarity (fair).Partial
- Tool/resource definitions use about 513 tokens (~46/item across 11 items; 11 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management80
- Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage71
- 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
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 2 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "remove_document" implies "remove" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
- An AI judge read all 11 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the Scholar RAG MCP server?
Scholar RAG MCP runs locally as a PyPI package, launched with uvx scholar-rag-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · scholar-rag-mcp
claude mcp add notwhiteblank-scholar-rag-mcp -- uvx scholar-rag-mcp
{
"mcpServers": {
"notwhiteblank-scholar-rag-mcp": {
"command": "uvx",
"args": [
"scholar-rag-mcp"
]
}
}
} {
"servers": {
"notwhiteblank-scholar-rag-mcp": {
"command": "uvx",
"args": [
"scholar-rag-mcp"
]
}
}
} codex mcp add notwhiteblank-scholar-rag-mcp -- uvx scholar-rag-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"notwhiteblank-scholar-rag-mcp": {
"type": "local",
"command": [
"uvx",
"scholar-rag-mcp"
],
"enabled": true
}
}
} openclaw mcp add notwhiteblank-scholar-rag-mcp --command uvx --arg scholar-rag-mcp
mcp_servers:
notwhiteblank-scholar-rag-mcp:
command: "uvx"
args: ["scholar-rag-mcp"] {
"McpServers": {
"notwhiteblank-scholar-rag-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"scholar-rag-mcp"
]
}
}
} assistant mcp add notwhiteblank-scholar-rag-mcp -t stdio -c uvx -a scholar-rag-mcp
{
"mcpServers": {
"notwhiteblank-scholar-rag-mcp": {
"command": "uvx",
"args": [
"scholar-rag-mcp"
]
}
}
} 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.
- 20 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 19 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 17 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 16 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.
- 15 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- 14 Sept 26 −14
- Malware scan: pass → unverified ▼ security
- 12 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 11 Sept 26 −15
- Malware scan: pass → unverified ▼ security
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 20 Sept 2026 · Analysed pypi/scholar-rag-mcp@0.4.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 48 packages
| Packages resolved | 48 |
|---|---|
| Stale | 3 |
| No linked repository | 1 |
| 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 →
add_document ~39
Asynchronously ingest a single PDF into an existing kb; returns a job_id.
| Name | Type | Req | Description |
|---|---|---|---|
| kb | string | yes | – |
| pdf_path | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
create_kb ~74
Asynchronously create a kb from all PDFs in a folder; returns a job_id.
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_max | integer | – | – |
| chunk_min | integer | – | – |
| chunk_overlap | integer | – | – |
| folder_path | string | yes | – |
| kb_name | string | yes | – |
| skip_existing | boolean | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
delete_kb ~36
Two-phase kb deletion: preview and confirm token, else full deletion.
| Name | Type | Req | Description |
|---|---|---|---|
| confirm_token | – | – | – |
| kb | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_document ~39
Overview of a document: metadata, abstract, section outline and total character count.
| Name | Type | Req | Description |
|---|---|---|---|
| doc_id | string | yes | – |
| kb | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_document_text ~59
Paginated reading of the full text or a single section of a document.
| Name | Type | Req | Description |
|---|---|---|---|
| doc_id | string | yes | – |
| kb | string | yes | – |
| page | integer | – | – |
| page_size | integer | – | – |
| section | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_job ~28
Query the status, progress and result of a background job.
| Name | Type | Req | Description |
|---|---|---|---|
| job_id | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_documents ~46
Paginated browse of documents in a kb.
| Name | Type | Req | Description |
|---|---|---|---|
| kb | string | yes | – |
| page | integer | – | – |
| page_size | integer | – | – |
| sort | string | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_kbs ~18
List all knowledge bases with metadata and status.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
remove_document ~37
Synchronously delete a document from qdrant, catalog and disk.
| Name | Type | Req | Description |
|---|---|---|---|
| doc_id | string | yes | – |
| kb | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
search_chunks ~60
Semantic search over chunk bodies of a kb with metadata filters, embedding and rerank scores.
| Name | Type | Req | Description |
|---|---|---|---|
| kb | string | yes | – |
| metadata_filter | – | – | – |
| min_score | – | – | – |
| query | string | yes | – |
| top_k | integer | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
search_documents ~77
Document-level PubMed-style search with optional FTS query and metadata filters.
| Name | Type | Req | Description |
|---|---|---|---|
| authors | – | – | – |
| journal | – | – | – |
| kb | string | yes | – |
| page | integer | – | – |
| page_size | integer | – | – |
| query | – | – | – |
| title | – | – | – |
| year_from | – | – | – |
| year_to | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
What is the Scholar RAG MCP server?
Scholar RAG MCP is listed in the public MCP registry as io.github.notwhiteblank/scholar-rag-mcp. Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management. This page covers its PyPI package (scholar-rag-mcp).
Is the Scholar RAG MCP server safe to use?
Scholar RAG MCP scores 57 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. 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 Scholar RAG MCP server expose?
Scholar RAG MCP exposes 11 tools: search_chunks, search_documents, list_documents, get_document, get_document_text, and 6 more. Their descriptions and schemas cost roughly 513 tokens of context every time the server is loaded.
Is the Scholar RAG MCP server still maintained?
Scholar RAG MCP is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.