[](https://mseep.ai/app/adityak74-mcp-scholarly)
# mcp-scholarly MCP server
[](https://smithery.ai/server/mcp-scholarly)
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 the `scholarly` library (free proxy pool)
- `search-google-web` — Google web search via the [SerpBase API](https://serpbase.dev). Optional; only registered when `SERPBASE_API_KEY` is set. Get a key at https://serpbase.dev/dashboard/api-keys (free tier available).
## 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`
Development/Unpublished Servers Configuration
```
"mcpServers": {
"mcp-scholarly": {
"command": "uv",
"args": [
"--directory",
"/Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly",
"run",
"mcp-scholarly"
]
}
}
```
Published Servers Configuration
```
"mcpServers": {
"mcp-scholarly": {
"command": "uvx",
"args": [
"mcp-scholarly"
]
}
}
```
or if you are using Docker
Published Docker Servers Configuration
```
"mcpServers": {
"mcp-scholarly": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"mcp/scholarly"
]
}
}
```
### Installing via Smithery
To install mcp-scholarly for Claude Desktop automatically via [Smithery](https://smithery.ai/server/mcp-scholarly):
```bash
npx -y @smithery/cli install mcp-scholarly --client claude
```
## Development
### Building and Publishing
To prepare the package for distribution:
1. Sync dependencies and update lockfile:
```bash
uv sync
```
2. Build package distributions:
```bash
uv build
```
This will create source and wheel distributions in the `dist/` directory.
3. Publish to PyPI:
```bash
uv publish
```
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--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](https://github.com/modelcontextprotocol/inspector).
You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:
```bash
npx @modelcontextprotocol/inspector uv --directory /Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly run mcp-scholarly
```
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
## Using with zorp
[zorp](https://github.com/aviskaar/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`.
```bash
zorp-agent --yes \
--mcp "stdio:scholarly:uv:run:mcp-scholarly" \
validate ""
```
Or configure it once, so every run picks it up:
```toml
# .zorp/mcp.toml
[[server]]
name = "scholarly"
transport = "stdio"
command = "uv"
args = ["run", "mcp-scholarly"]
trust = "sandbox"
timeout_secs = 60
```
Notes measured against zorp's transport, not assumed:
- `search-arxiv` answers in about 1 second. zorp's default stdio read
budget is 30 seconds, so the default is comfortable. `timeout_secs = 60`
above is headroom for `search-google-scholar`, which goes through
`scholarly` and 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.