mcp-scholarly MCP server logo

mcp-scholarly MCP server

Community
adityak74

A MCP server to search for accurate academic articles.

Publisheradityak74
Repositorymcp-scholarly
LanguagePython
Forks
25
Stars
177
Available tools
0
Transport typestdio
Categories
Other
LicenseMIT
Links
GitHub
  • Connect tools to AI workflows

    mcp-scholarly MCP server exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    177 stars and 25 forks from the linked repository.

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mcp-scholarly MCP server

smithery badge

A MCP server to search for accurate academic articles. More scholarly vendors will be added soon.

demo1.jpeg

image

star-history-202551

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:

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
  1. Build package distributions:
bash
uv build

This will create source and wheel distributions in the dist/ directory.

  1. 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.

You can launch the MCP Inspector via 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.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "adityak74-mcp-scholarly": {
      "command": "uvx",
      "args": [
        "mcp-scholarly"
      ]
    }
  }
}

Use mcp-scholarly MCP server MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once mcp-scholarly MCP server is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add mcp-scholarly MCP server as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the mcp-scholarly MCP server MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "adityak74-mcp-scholarly": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-scholarly"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the mcp-scholarly MCP server MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the mcp-scholarly MCP server MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use mcp-scholarly MCP server to help me with this task?
mcp-scholarly MCP server
Sure. I read it.
Here is what I found using mcp-scholarly MCP server.

Frequently asked questions

What is the mcp-scholarly MCP server MCP server used for?

mcp-scholarly MCP server is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use mcp-scholarly MCP server MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use mcp-scholarly MCP server with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use mcp-scholarly MCP server MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With mcp-scholarly MCP server connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect mcp-scholarly MCP server MCP to TypingMind?

mcp-scholarly MCP server runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does mcp-scholarly MCP server MCP provide in TypingMind?

mcp-scholarly MCP server exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use mcp-scholarly MCP server MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If mcp-scholarly MCP server requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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