Connect AI agents to the Parallel AI Task MCP server

Setup

1. Install the SDK

   • Node.js
   • Python

   npm install @scalekit-sdk/node dotenv

2. Set your credentials

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

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

3. Create the Parallel AI Task MCP connection

   In AgentKit > Connections, create a Parallel AI Task MCP connection. The name you give it is the connection_name your code passes. See Configure connections.

   Console steps with screenshots

   Register your Parallel AI API key with Scalekit so it can authenticate and proxy task requests on behalf of your users. Parallel AI Task MCP uses API key authentication.

   1. Get a Parallel AI API key

      • Go to platform.parallel.ai and sign in or create an account.

      • Navigate to Settings → API Keys and click Create new key.

      • Give the key a name (e.g., Agent Auth) and copy it immediately — it will not be shown again.

   2. Create a connection in Scalekit

      • In Scalekit dashboard, go to AgentKit > Connections. Find Parallel AI Task MCP and click Create.

      • Note the Connection name — you will use this as connection_name in your code (e.g., parallelaitaskmcp).

   3. Add a connected account

      Connected accounts link a specific user identifier in your system to a Parallel AI API key. Add one in the dashboard to test. In production, each user adds their own through the authorization link: they enter their credentials on the page it opens.

      In the dashboard, to test

      • Open the connection you created and click the Connected Accounts tab → Add account.

      • Fill in:

        • Your User’s ID — a unique identifier for this user in your system (e.g., user_123)
        • Parallel AI API Key — the key you copied in step 1

      • Click Save.

      From your backend, if your app already has the credentials

      For example, when users enter them on a settings page in your app:

      • Node.js
      • Python

      1
      import { ConnectorStatus } from '@scalekit-sdk/node'
      2
      
      3
      const authorizationDetails = {
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        details: {
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          case: 'staticAuth',
      6
          value: { details: { token: 'your-parallel-ai-api-key' } },
      7
        },
      8
      };
      9
      
      10
      let { connectedAccount } = await scalekit.actions.upsertConnectedAccount({
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        connectionName: 'parallelaitaskmcp',
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        identifier: 'user_123',       // your user's unique ID
      13
        authorizationDetails,
      14
      });
      15
      
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      // Make sure the account is ACTIVE before the first tool call.
      17
      if (connectedAccount?.status !== ConnectorStatus.ACTIVE) {
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        ({ connectedAccount } = await scalekit.actions.upsertConnectedAccount({
      19
          connectionName: 'parallelaitaskmcp',
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          identifier: 'user_123',
      21
          authorizationDetails,
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        }));
      23
      }

4. Authorize a user and make your first call

   • Node.js
   • Python

   1
   import { ScalekitClient } from '@scalekit-sdk/node'
   2
   import 'dotenv/config'
   3
   import { createInterface } from 'node:readline/promises'
   4
   
   5
   const scalekit = new ScalekitClient(
   6
     process.env.SCALEKIT_ENVIRONMENT_URL,
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     process.env.SCALEKIT_CLIENT_ID,
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     process.env.SCALEKIT_CLIENT_SECRET,
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   )
   10
   const actions = scalekit.actions
   11
   
   12
   const connector = 'parallelaitaskmcp'
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   const identifier = 'user_123'
   14
   
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   // Generate an authorization link for the user
   16
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   17
   console.log('Authorize Parallel AI Task MCP:', link)
   18
   const rl = createInterface({ input: process.stdin, output: process.stdout })
   19
   await rl.question('Press Enter after authorizing...')
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   rl.close()
   21
   
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   // Make your first call
   23
   const result = await actions.executeTool({
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     connector,
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     identifier,
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     toolName: 'parallelaitaskmcp_get_result_markdown',
   27
     toolInput: { taskRunOrGroupId: 'YOUR_TASKRUNORGROUPID' },
   28
   })
   29
   console.log(result)

   npx tsx quickstart.mts

   Each user opens the link once and enters their Parallel AI Task MCP server credentials there. If your app already has a user's credentials, add the account from your backend instead, as the console steps above show. See Authorize a user for the full flow and statuses.

Workflows

result = actions.execute_tool(
    connection_name='parallelaitaskmcp',
    identifier='user_123',
    tool_name='parallelaitaskmcp_create_deep_research',
    tool_input={
        "input": "Analyze the competitive landscape of AI coding assistants in 2025",
    },
)
print("Task created:", result)

const result = await actions.executeTool({
  connector: 'parallelaitaskmcp',
  identifier: 'user_123',
  toolName: 'parallelaitaskmcp_create_deep_research',
  toolInput: {
    input: 'Analyze the competitive landscape of AI coding assistants in 2025',
  },
});
console.log('Task created:', result);