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Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.
Scholar RAG MCP is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 73/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Scholar RAG MCP to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
uvx scholar-rag-mcp==0.4.0Review the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-09-04T01:53:07.362043Z",
"publishedAt": "2026-09-04T01:53:07.362043Z",
"statusChangedAt": "2026-09-04T01:53:07.362043Z"
}
},
"server": {
"name": "io.github.notwhiteblank/scholar-rag-mcp",
"title": "Scholar RAG MCP",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "0.4.0",
"packages": [
{
"version": "0.4.0",
"transport": {
"type": "stdio"
},
"identifier": "scholar-rag-mcp",
"registryType": "pypi",
"environmentVariables": [
{
"name": "SCHOLAR_RAG_DATA_DIR",
"description": "Data root directory (knowledge bases, caches, jobs)"
},
{
"name": "SCHOLAR_RAG_CHAT_BASE_URL",
"isRequired": true,
"description": "OpenAI-compatible chat endpoint for annotation/keywords (e.g. local vLLM)"
},
{
"name": "SCHOLAR_RAG_EMBED_BASE_URL",
"isRequired": true,
"description": "OpenAI-compatible embeddings endpoint"
},
{
"name": "SCHOLAR_RAG_RERANK_BASE_URL",
"isRequired": true,
"description": "Rerank endpoint (/v1/rerank)"
}
]
}
],
"repository": {
"url": "https://github.com/notwhiteblank/scholar-rag-mcp",
"source": "github"
},
"description": "Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management."
}
}