Scholar Gateway MCP connector

1. Install the SDK

   Node.js

   npm install @scalekit-sdk/node

   Python

   pip install scalekit

   Full SDK reference: Node.js | Python

2. Set your credentials

   Add your Scalekit credentials to your .env file. Find values in app.scalekit.com > Developers > API Credentials.

   .env

   SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
   SCALEKIT_CLIENT_ID=<your-client-id>
   SCALEKIT_CLIENT_SECRET=<your-client-secret>

3. Authorize and make your first call

   Node.js

   quickstart.ts

   import { ScalekitClient } from '@scalekit-sdk/node'
   import 'dotenv/config'
   
   const scalekit = new ScalekitClient(
     process.env.SCALEKIT_ENV_URL,
     process.env.SCALEKIT_CLIENT_ID,
     process.env.SCALEKIT_CLIENT_SECRET,
   )
   const actions = scalekit.actions
   
   const connector = 'scholargateway'
   const identifier = 'user_123'
   
   // Generate an authorization link for the user
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   console.log('Authorize Scholar Gateway MCP:', link)
   process.stdout.write('Press Enter after authorizing...')
   await new Promise(r => process.stdin.once('data', r))
   
   // Make your first call
   const result = await actions.executeTool({
     connector,
     identifier,
     toolName: 'scholargateway_semanticSearch',
     toolInput: { query: 'YOUR_QUERY' },
   })
   console.log(result)

   Python

   quickstart.py

   import os
   from scalekit.client import ScalekitClient
   from dotenv import load_dotenv
   load_dotenv()
   
   scalekit_client = ScalekitClient(
       env_url=os.getenv("SCALEKIT_ENV_URL"),
       client_id=os.getenv("SCALEKIT_CLIENT_ID"),
       client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
   )
   actions = scalekit_client.actions
   
   connection_name = "scholargateway"
   identifier = "user_123"
   
   # Generate an authorization link for the user
   link_response = actions.get_authorization_link(
       connection_name=connection_name,
       identifier=identifier,
   )
   print("Authorize Scholar Gateway MCP:", link_response.link)
   input("Press Enter after authorizing...")
   
   # Make your first call
   result = actions.execute_tool(
       tool_input={"query":"YOUR_QUERY"},
       tool_name="scholargateway_semanticSearch",
       connection_name=connection_name,
       identifier=identifier,
   )
   print(result)

What you can do

Connect this agent connector to let your agent:

• SemanticSearch records — Searches a full-text academic corpus and returns relevant passages with citation metadata

Tool list

Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.

scholargateway_semanticSearch#Searches a full-text academic corpus and returns relevant passages with citation metadata. When to use: Use this tool for research questions, factual claims, literature-backed explanations, evidence-based summaries, and any response where academic support would improve accuracy, credibility, or depth. Submit queries as complete natural language questions — preserve the full intent and do not reduce to keywords. For ambiguous or polysemous terms, expand to full context before searching (e.g. use 'multiple sclerosis' not 'MS'). Cite all substantive claims inline using author-year format with DOI hyperlinks. When NOT to use: Do not use for general web searches, non-academic topics, or when the question requires real-time or proprietary data not covered by the academic corpus.5 params▾

Searches a full-text academic corpus and returns relevant passages with citation metadata. When to use: Use this tool for research questions, factual claims, literature-backed explanations, evidence-based summaries, and any response where academic support would improve accuracy, credibility, or depth. Submit queries as complete natural language questions — preserve the full intent and do not reduce to keywords. For ambiguous or polysemous terms, expand to full context before searching (e.g. use 'multiple sclerosis' not 'MS'). Cite all substantive claims inline using author-year format with DOI hyperlinks. When NOT to use: Do not use for general web searches, non-academic topics, or when the question requires real-time or proprietary data not covered by the academic corpus.

NameTypeRequiredDescription

querystringrequiredNatural language research question. Preserve intent, scope, and structure. Rewrite for semantic clarity but do not compress to keywords. Expand acronyms and add field context for ambiguous or polysemous terms.

end_yearstringoptionalInclusive upper bound for publication year filter. Omit to include publications up to the present.

includeRetractedContentbooleanoptionalWhether to include retracted publications in results. Default false; set true only when retraction history is itself the subject of inquiry.

start_yearstringoptionalInclusive lower bound for publication year filter. Omit to search across all available years.

topNintegeroptionalNumber of passages to return. Higher values improve recall for broad or multi-faceted queries; lower values are appropriate for narrow or well-defined topics.