MCP Scholarly Server
The MCP Scholarly Server allows you to search for academic articles across multiple scholarly platforms:
Search arXiv for articles using keywords
Search Google Scholar for articles using keywords
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Scholarly Server search for recent papers about quantum computing algorithms"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-scholarly MCP server
A MCP server to search for accurate academic articles. More scholarly vendors will be added soon.
Search tools
search-arxiv— arXiv search (no key needed)search-google-scholar— Google Scholar via thescholarlylibrary (free proxy pool)search-google-web— Google web search via the SerpBase API. Optional; only registered whenSERPBASE_API_KEYis set. Get a key at https://serpbase.dev/dashboard/api-keys (free tier available).

Related MCP server: arXiv MCP Server
Components
Tools
The server implements one tool:
search-arxiv: Search arxiv for articles related to the given keyword.
Takes "keyword" as required string arguments
Quickstart
Install
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
or if you are using Docker
Installing via Smithery
To install mcp-scholarly for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-scholarly --client claudeDevelopment
Building and Publishing
To prepare the package for distribution:
Sync dependencies and update lockfile:
uv syncBuild package distributions:
uv buildThis will create source and wheel distributions in the dist/ directory.
Publish to PyPI:
uv publishNote: You'll need to set PyPI credentials via environment variables or command flags:
Token:
--tokenorUV_PUBLISH_TOKENOr username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory /Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly run mcp-scholarlyUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
Using with zorp
zorp needs a search-capable MCP tool
before validate will run. This server satisfies that check, because zorp
matches on a search verb in the tool name and these tools are called
search-arxiv and search-google-scholar.
zorp-agent --yes \
--mcp "stdio:scholarly:uv:run:mcp-scholarly" \
validate "<your research question>"Or configure it once, so every run picks it up:
# .zorp/mcp.toml
[[server]]
name = "scholarly"
transport = "stdio"
command = "uv"
args = ["run", "mcp-scholarly"]
trust = "sandbox"
timeout_secs = 60Notes measured against zorp's transport, not assumed:
search-arxivanswers in about 1 second. zorp's default stdio read budget is 30 seconds, so the default is comfortable.timeout_secs = 60above is headroom forsearch-google-scholar, which goes throughscholarlyand a free proxy pool and is far less predictable.Logging goes to stderr. Nothing but JSON-RPC reaches stdout, which is what zorp's newline-delimited framing requires.
An empty keyword comes back as an MCP tool error rather than an empty result set. zorp cares about that distinction: a failed search that looks like "no prior work" would put a wrong novelty score into an evidence record.
arxiv returns best-effort matches for any query, including nonsense, so a non-empty result set is not by itself evidence that prior work exists. The tool description says so, since that is the text the model reads.
Available Tools
2 toolssearch-arxivC
Search arxiv for articles related to the given keyword.
Inputs: keyword (required).
search-google-scholarC
Search google scholar for articles related to the given keyword.
Inputs: keyword (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one searches ArXiv specifically, while the other searches Google Scholar. There is no overlap in their functionality, and an agent can easily choose the appropriate tool based on the desired academic database.
Both tools follow a consistent verb_noun pattern with 'search' as the verb and the database name as the noun (arxiv, google-scholar). The naming is uniform and predictable across the set.
With only 2 tools, the server feels thin for a 'Scholarly Server' that might be expected to handle broader academic tasks. While search is a core function, typical scholarly workflows could benefit from additional tools like fetching article details, citations, or managing references.
The server is severely incomplete for a scholarly domain, as it only provides search functionality without any tools for retrieving full articles, accessing metadata, or performing follow-up actions like citation analysis. This leaves significant gaps that could hinder agent workflows.
Maintenance
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