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Connect AI agents to the Exa MCP server

Vendor MCP
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The Exa MCP connector routes your AI agent's tool calls to Exa's own MCP server through Scalekit. Each user connects their own Exa API key once, and Scalekit sends it with every call, so your agent never handles credentials. It comes with 3 tools.

Tools
3
What they doRead · write · destructive
2 · 1 · 02 read1 write0 destructive
Users sign in with

What you can do

  • Search the web: find current news, facts, people, and companies and get clean page content back
  • Read webpages: fetch the full content of specific URLs as clean markdown

Setup

  1. Install the SDK

    Terminal window
    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.

    .env
    SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
    SCALEKIT_CLIENT_ID=<your-client-id>
    SCALEKIT_CLIENT_SECRET=<your-client-secret>
  3. Create the Exa MCP connection

    In AgentKit > Connections, create an Exa MCP connection. The name you give it is the connection_name your code passes. See Configure connections.

    Console steps with screenshots

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

    1. Generate an Exa API key

      • Sign in to dashboard.exa.ai/api-keys. Under Management, click API Keys.

      • Click + Create Key, enter a name (e.g., Agent Auth), and confirm.

      • In the Secret Key column, click the eye icon to reveal the key and copy it. Store it somewhere safe — you will not be able to view it again.

      Exa dashboard API Keys page showing existing keys and the + Create Key button

    2. Create a connection in Scalekit

      • In Scalekit dashboard, go to AgentKit > Connections > Create Connection. Find Exa MCP and click Create.

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

    3. Add a connected account

      Connected accounts link a specific user identifier in your system to an Exa 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)
        • API Key — the Exa API 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:

      import { ConnectorStatus } from '@scalekit-sdk/node'
      const authorizationDetails = {
      details: {
      case: 'staticAuth',
      value: { details: { api_key: 'your-exa-api-key' } },
      },
      };
      let { connectedAccount } = await scalekit.actions.upsertConnectedAccount({
      connectionName: 'examcp',
      identifier: 'user_123',
      authorizationDetails,
      });
      // Make sure the account is ACTIVE before the first tool call.
      if (connectedAccount?.status !== ConnectorStatus.ACTIVE) {
      ({ connectedAccount } = await scalekit.actions.upsertConnectedAccount({
      connectionName: 'examcp',
      identifier: 'user_123',
      authorizationDetails,
      }));
      }
  4. Authorize a user and make your first call

    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 = 'examcp'
    const identifier = 'user_123'
    // Generate an authorization link for the user
    const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
    console.log('Authorize Exa 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: 'examcp_web_fetch_exa',
    toolInput: { urls: [] },
    })
    console.log(result)
    Terminal window
    npx tsx quickstart.mts

    Each user opens the link once and enters their Exa 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.

Tools

Pass the exact name to execute_tool
Try in PlaygroundRequest a tool
  • examcp_web_fetch_exaRead one or more webpages and return their full content as clean markdown.Read-only

    Exa Web Fetch

    Read one or more webpages and return their full content as clean markdown. Use when you have specific URLs to read, or to get full content after a web search returns insufficient highlights. Supports batching multiple URLs in a single call.

    Inputs

    urlsarrayrequired
    One or more URLs to fetch. Batch multiple URLs in a single call.
    maxCharactersnumber
    Maximum characters to extract per page (default: 3000).
  • examcp_web_search_exaSearch the web and get clean, ready-to-use content.Read-only

    Exa Web Search

    Search the web and get clean, ready-to-use content. Best for current information, news, facts, people, and companies. Describe the ideal page rather than using keywords (e.g. 'blog post comparing React and Vue performance'). Use category:people or category:company to search LinkedIn profiles or companies.

    Inputs

    objectivestringrequired
    Goal for this search turn; say which documents should rank first, which should be excluded, and what specific facts or figures to pull from them.
    querystringrequired
    Natural language search query. Should be a semantically rich description of the ideal page, not just keywords. Optionally include category:<type> (company, people) to focus results — e.g. 'category:people John Doe software engineer'.
    numResultsnumber
    Number of search results to return (default: 10).
  • examcp_agent_runStart or resume an Exa Agent run for multi-step research, list-building, or enrichment.Write

    Agent Run

    Start or resume an Exa Agent run for multi-step research, list-building, or enrichment. Use it instead of running many searches yourself when a task needs multiple searches, cross-source verification, or a structured table of results. Long-running: returns a run ID; resume with runId when the tool reports the run is still running. Not for a single question or lookup (use web_search_exa) or reading a known URL (use web_fetch_exa). An interrupted tool call is not an explicit cancellation request.

    Inputs

    budgetobject
    Optional per-run spend and duration limits for the metered auto and ultra efforts. Set it when the user names a cost or time limit.
    dataSourcesarray
    Optional Exa Connect providers to enable for this run.
    effortstring
    Agent effort level. Defaults to low.one of minimallowmediumhighxhighultraauto
    inputobject
    No description.
    outputSchemaobject
    JSON Schema describing the desired research output. Prefer a top-level object with bounded arrays and source/evidence fields.
    previousRunIdstring
    ID of a completed prior run to use as context for a new run.
    querystring
    Natural-language research or enrichment objective. Provide query or runId, not both.
    runIdstring
    ID returned by an earlier call. Use it to check or continue waiting for the same run. Do not start a duplicate run.
    systemPromptstring
    Optional system-level guidance for the Agent.