Connect AI agents to the Deepgram MCP server

1. Install the SDK

   Node.js

   npm install @scalekit-sdk/node dotenv

   Python

   pip install scalekit-sdk-python python-dotenv

   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.mts

   import { ScalekitClient } from '@scalekit-sdk/node'
   import 'dotenv/config'
   import { createInterface } from 'node:readline/promises'
   
   const scalekit = new ScalekitClient(
     process.env.SCALEKIT_ENVIRONMENT_URL,
     process.env.SCALEKIT_CLIENT_ID,
     process.env.SCALEKIT_CLIENT_SECRET,
   )
   const actions = scalekit.actions
   
   const connector = 'deepgrammcp'
   const identifier = 'user_123'
   
   // Generate an authorization link for the user
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   console.log('Authorize Deepgram MCP:', link)
   const rl = createInterface({ input: process.stdin, output: process.stdout })
   await rl.question('Press Enter after authorizing...')
   rl.close()
   
   // Make your first call
   const result = await actions.executeTool({
     connector,
     identifier,
     toolName: 'deepgrammcp_search_deepgram_knowledge_sources',
     toolInput: { query: 'YOUR_QUERY' },
   })
   console.log(result)

   npx tsx quickstart.mts

   Python

   quickstart.py

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

   python quickstart.py

What you can do

Connect this agent connector to let your agent:

• Search deepgram knowledge sources — Search Deepgram documentation and knowledge sources for the most relevant results for a given query

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.

deepgrammcp_search_deepgram_knowledge_sources#Search Deepgram documentation and knowledge sources for the most relevant results for a given query.1 param▾

Search Deepgram documentation and knowledge sources for the most relevant results for a given query.

NameTypeRequiredDescription

querystringrequiredA single, well-formed natural-language query to search Deepgram knowledge sources. Must be a complete sentence.