Using the Vercel AI SDK with Gram-hosted MCP servers

The Vercel AI SDK supports remote MCP servers through its experimental MCP client feature. This allows you to give AI models direct access to your tools and APIs by connecting to Gram-hosted MCP servers.

This guide shows you how to connect the Vercel AI SDK to a Gram-hosted MCP server using an example Push Advisor API . You’ll learn how to create an MCP server from an OpenAPI document, set up the connection, configure authentication, and use natural language to query the example API.

Find the full code and OpenAPI document in the Push Advisor API repository .

Prerequisites

You’ll need:

A Gram account
An OpenAI API key or Anthropic API key
A Node.js environment set up on your machine
Basic familiarity with TypeScript, JavaScript, and making API requests

Creating a Gram MCP server

If you already have a Gram MCP server configured, you can skip to connecting the Vercel AI SDK to your Gram-hosted MCP server. For an in-depth guide to how Gram works and to creating a Gram-hosted MCP server, check out our introduction to Gram.

Setting up a new Gram project

In the Gram dashboard , click New Project to start the guided setup flow for creating a toolset and MCP server.

When prompted, upload the Push Advisor OpenAPI document .

Follow the steps to configure a toolset and publish an MCP server. At the end of the setup, you’ll have a Gram-hosted MCP server ready to use.

For this guide, we’ll use the public server URL https://app.getgram.ai/mcp/canipushtoprod.

For authenticated servers, you’ll need an API key. Generate an API key in the Settings tab.

Connecting the Vercel AI SDK to your Gram-hosted MCP server

The Vercel AI SDK supports MCP servers through the experimental_createMCPClient function with Streamable HTTP transport. Here’s how to connect to your Gram-hosted MCP server:

Installation and setup

First, install the Vercel AI SDK, the MCP SDK, and an AI provider SDK. The samples in this guide use OpenAI, but you can also use Anthropic or other providers.

Run the following:


npm install ai @ai-sdk/openai @modelcontextprotocol/sdk dotenv
# if using Anthropic, add
# npm install @ai-sdk/anthropic

Project configuration

Configure your project for ES modules by adding "type": "module" to your package.json:


{
  "name": "my-gram-integration",
  "version": "1.0.0",
  "type": "module",
  "main": "index.js",
  "dependencies": {
    "ai": "^5.0.21",
    "@ai-sdk/openai": "^2.0.19",
    "@modelcontextprotocol/sdk": "^1.17.3",
    "dotenv": "^16.0.0"
  }
}

Environment variables

Create a .env file in your project root to store your API keys:


OPENAI_API_KEY=your-openai-api-key-here
ANTHROPIC_API_KEY=your-anthropic-api-key-here  # if using Anthropic
GRAM_API_KEY=your-gram-api-key-here  # for authenticated Gram servers

Load these environment variables at the top of your JavaScript files:


import dotenv from "dotenv";
dotenv.config();

Basic connection (public server)

Here’s a basic example using a public Gram MCP server with Streamable HTTP transport:


import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";
import { stepCountIs } from "ai";
import dotenv from "dotenv";
 
// Load environment variables
dotenv.config();
 
// Create HTTP transport for your Gram-hosted MCP server
const httpTransport = new StreamableHTTPClientTransport(
  new URL("https://app.getgram.ai/mcp/canipushtoprod"),
);
 
// Create an MCP client using the Streamable HTTP transport
const mcpClient = await createMCPClient({
  transport: httpTransport,
});
 
// Get tools from the MCP server
const tools = await mcpClient.tools();
 
// Use the tools with AI SDK
const result = await streamText({
  model: openai("gpt-4o"),
  tools,
  prompt: "Can I push to production today? Please check the status.",
  stopWhen: stepCountIs(2), // Ensure text response after tool call
  onStepFinish: ({ toolCalls, toolResults }) => {
    // Log tool calls as they complete
    toolCalls?.forEach((call) => {
      console.log(`🔧 Called tool: ${call.toolName}`);
    });
 
    toolResults?.forEach((result) => {
      console.log(`📊 Tool result: ${JSON.stringify(result.output)}`);
    });
  },
  onFinish: async () => {
    await mcpClient.close();
  },
});
 
// Print the response
console.log("AI Response:");
for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}

Authenticated connection

For authenticated Gram MCP servers, include your Gram API key in the headers:


import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";
import { stepCountIs } from "ai";
import dotenv from "dotenv";
 
// Load environment variables
dotenv.config();
 
const GRAM_API_KEY = process.env.GRAM_API_KEY;
 
if (!GRAM_API_KEY) {
  throw new Error("Missing GRAM_API_KEY environment variable");
}
 
// Create HTTP transport with authentication
const httpTransport = new StreamableHTTPClientTransport(
  new URL("https://app.getgram.ai/mcp/canipushtoprod"),
  {
    headers: {
      Authorization: `Bearer ${GRAM_API_KEY}`,
    },
  },
);
 
let mcpClient;
 
try {
  // Create an authenticated MCP client
  mcpClient = await createMCPClient({
    transport: httpTransport,
  });
 
  const tools = await mcpClient.tools();
 
  const { text, toolCalls, toolResults } = await generateText({
    model: openai("gpt-4o"),
    tools,
    prompt: "Can I push to production today?",
    stopWhen: stepCountIs(2), // Ensure text response after tool call
  });
 
  // Access tool call information
  if (toolCalls && toolCalls.length > 0) {
    console.log("Tool calls made:");
    toolCalls.forEach((call) => {
      console.log(`- Called tool: ${call.toolName}`);
      console.log(`- With args: ${JSON.stringify(call.input)}`);
    });
  }
 
  // Access tool results
  if (toolResults && toolResults.length > 0) {
    console.log("Tool results:");
    toolResults.forEach((result) => {
      console.log(`- Tool result: ${JSON.stringify(result.output)}`);
    });
  }
 
  console.log(`Final response: ${text}`);
} finally {
  if (mcpClient) {
    await mcpClient.close();
  }
}

Understanding the configuration

Here’s what each parameter in the createMCPClient configuration does:

StreamableHTTPClientTransport uses Streamable HTTP transport (as opposed to SSE or stdio).
new URL(...) adds your Gram-hosted MCP server URL.
headers adds optional HTTP headers for authentication (passed as the second parameter to the transport constructor).

Tool filtering and schema approaches

The Vercel AI SDK supports two approaches to working with MCP tools: schema discovery and schema definition.

Schema discovery (recommended for Gram)

Schema discovery is the simplest approach, where all tools offered by the server are listed automatically:


// Discover all tools from the server
const tools = await mcpClient.tools();
 
// All tools are now available for use
const result = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "Is it safe to deploy today?",
});

Schema definition with TypeScript

For better type safety and IDE support, you can define schemas explicitly:


import { z } from "zod";
 
// Define specific tools with their schemas
const tools = await mcpClient.tools({
  schemas: {
    can_i_push_to_prod: {
      inputSchema: z.object({}),
    },
    vibe_check: {
      inputSchema: z.object({}),
    },
  },
});
 
// Now only the specified tools are available
const result = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "What is the vibe today?",
});

When you define schemas, the client only pulls the explicitly defined tools, even if the server offers additional tools.

Limiting tools with activeTools

You can also limit which tools are available to the model using the activeTools parameter:


const tools = await mcpClient.tools();
 
const result = await generateText({
  model: openai("gpt-4o"),
  tools,
  activeTools: ["can_i_push_to_prod"], // Only this tool is available
  prompt: "What is the vibe today?",
});

Working with responses

The Vercel AI SDK provides different ways to handle MCP tool calls depending on whether you use generateText or streamText.

Using generateText

With generateText, you get access to tool calls and results in the response:


// Create HTTP transport for Gram MCP server
const httpTransport = new StreamableHTTPClientTransport(
  new URL("https://app.getgram.ai/mcp/canipushtoprod"),
);
 
const mcpClient = await createMCPClient({
  transport: httpTransport,
});
 
try {
  const tools = await mcpClient.tools();
 
  const { text, toolCalls, toolResults } = await generateText({
    model: openai("gpt-4o"),
    tools,
    prompt: "Can I push to production today?",
  });
 
  // Access tool call information
  toolCalls.forEach((call) => {
    console.log(`Called tool: ${call.toolName}`);
    console.log(`With args: ${JSON.stringify(call.input)}`);
  });
 
  // Access tool results
  toolResults.forEach((result) => {
    console.log(`Tool result: ${JSON.stringify(result.output)}`);
  });
 
  console.log(`Final response: ${text}`);
} finally {
  await mcpClient.close();
}

Using streamText

With streamText, you can handle tool calls as they stream in:


// Create HTTP transport for Gram MCP server
const httpTransport = new StreamableHTTPClientTransport(
  new URL("https://app.getgram.ai/mcp/canipushtoprod"),
);
 
const mcpClient = await createMCPClient({
  transport: httpTransport,
});
 
const tools = await mcpClient.tools();
 
const result = await streamText({
  model: openai("gpt-4o"),
  tools,
  prompt: "What is the deployment status?",
  onStepFinish: ({ toolCalls, toolResults }) => {
    // Handle tool calls as they complete
    toolCalls?.forEach((call) => {
      console.log(`Tool called: ${call.toolName}`);
    });
 
    toolResults?.forEach((result) => {
      console.log(`Tool result: ${JSON.stringify(result.output)}`);
    });
  },
  onFinish: async ({ text, toolCalls, toolResults }) => {
    console.log(`Final text: ${text}`);
    console.log(`Total tool calls: ${toolCalls.length}`);
    await mcpClient.close();
  },
});
 
// Stream the text response
for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}

Error handling

The Vercel AI SDK includes error handling for MCP tool calls:

Connection errors

Handle MCP client connection failures:


import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
 
let mcpClient;
 
try {
  const httpTransport = new StreamableHTTPClientTransport(
    new URL("https://app.getgram.ai/mcp/canipushtoprod"),
    {
      headers: {
        Authorization: `Bearer ${GRAM_API_KEY}`,
      },
    },
  );
 
  mcpClient = await createMCPClient({
    transport: httpTransport,
  });
 
  const tools = await mcpClient.tools();
  // Use tools...
} catch (error) {
  console.error("Failed to connect to MCP server:", error);
  // Fallback logic or error handling
} finally {
  if (mcpClient) {
    await mcpClient.close();
  }
}

Tool execution errors

Handle errors that occur during tool execution:


import { generateText } from "ai";
import { stepCountIs } from "ai";
 
let mcpClient;
 
try {
  // ... MCP client setup ...
 
  const result = await generateText({
    model: openai("gpt-4o"),
    tools,
    prompt: "Deploy to production",
    stopWhen: stepCountIs(2),
    onStepFinish: ({ toolResults }) => {
      // Check for tool errors in the results
      toolResults?.forEach((result) => {
        if (result.type === "tool-result" && result.output.isError) {
          console.error(`Tool error in ${result.toolName}:`, result.output);
        } else if (result.type === "tool-result") {
          console.log(`Tool ${result.toolName} executed successfully`);
        }
      });
    },
  });
 
  // Also check for errors in the final steps
  result.steps.forEach((step) => {
    step.content.forEach((content) => {
      if (content.type === "tool-error") {
        console.error(`Tool error: ${content.error}`);
        console.error(`Tool name: ${content.toolName}`);
      }
    });
  });
} catch (error) {
  console.error("Generation error:", error);
} finally {
  if (mcpClient) {
    await mcpClient.close();
  }
}

For more advanced error handling patterns and troubleshooting, consult the Vercel AI SDK GitHub repository .

Generating text responses with tool calls

Important: By default, generateText() stops after executing tools and may return empty text responses. To get text responses after tool calls, you need to use multi-step generation.

Understanding the default behavior

When using tools with generateText(), the default behavior is as follows:

The model calls the appropriate tool(s).
The model receives tool results.
The model stops without generating a text response.

This is by design - some tool calls are meant to be fire-and-forget operations that don’t require text responses.

Getting text responses after tool calls

To generate text responses after tool calls, use stopWhen: stepCountIs(N), where N ≥ 2:


import { stepCountIs } from "ai";
 
const result = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "Can I push to production today?",
  stopWhen: stepCountIs(2), // Minimum: 1 step for tool call + 1 step for response
});
 
console.log(result.text);

This forces the model to:

Step 1: Execute tool calls
Step 2: Generate text response based on tool results

Multi-step tool calls

For complex workflows requiring multiple tools and responses, use higher step counts:


import { stepCountIs } from "ai";
 
let mcpClient;
 
try {
  // Create HTTP transport for Gram MCP server
  const httpTransport = new StreamableHTTPClientTransport(
    new URL("https://app.getgram.ai/mcp/canipushtoprod"),
  );
 
  mcpClient = await createMCPClient({
    transport: httpTransport,
  });
 
  const tools = await mcpClient.tools();
 
  const { text, steps } = await generateText({
    model: openai("gpt-4o"),
    tools,
    stopWhen: stepCountIs(5), // Stop after 5 steps if tools were called
    prompt: "Check if I can deploy and then tell me the vibe",
    onStepFinish: ({ text, toolCalls, toolResults }) => {
      console.log(`Step completed with ${toolCalls.length} tool calls`);
      if (text) {
        console.log(`Step text: ${text}`);
      }
    },
  });
 
  // Access all tool calls from all steps
  const allToolCalls = steps.flatMap((step) => step.toolCalls);
  console.log(`Total tool calls across all steps: ${allToolCalls.length}`);
  console.log(`Final response: ${text}`);
} finally {
  if (mcpClient) {
    await mcpClient.close();
  }
}

Client lifecycle management

Proper management of the MCP client lifecycle is important for resource efficiency:

Short-lived usage (recommended)

For single requests or short-lived operations, close the client when finished:


let mcpClient;
 
try {
  // Create HTTP transport for Gram MCP server
  const httpTransport = new StreamableHTTPClientTransport(
    new URL("https://app.getgram.ai/mcp/canipushtoprod"),
  );
 
  mcpClient = await createMCPClient({
    transport: httpTransport,
  });
 
  const tools = await mcpClient.tools();
  const result = await generateText({
    model: openai("gpt-4o"),
    tools,
    prompt: "Your prompt here",
  });
  // Process result...
} finally {
  if (mcpClient) {
    await mcpClient.close();
  }
}

Long-running applications

For server applications or CLI tools, you might keep the client open:


// Initialize once at startup
let mcpClient;
 
async function initializeMCPClient() {
  const httpTransport = new StreamableHTTPClientTransport(
    new URL("https://app.getgram.ai/mcp/canipushtoprod"),
  );
 
  mcpClient = await createMCPClient({
    transport: httpTransport,
  });
}
 
// Clean up on application shutdown
process.on("SIGINT", async () => {
  if (mcpClient) {
    await mcpClient.close();
    process.exit(0);
  }
});

Differences from other MCP integrations

The Vercel AI SDK’s approach to MCP differs from OpenAI and Anthropic’s native implementations:

Connection method

The Vercel AI SDK uses experimental_createMCPClient with Streamable HTTP or stdio transports.
OpenAI uses the tools array with type: "mcp" in the Responses API.
Anthropic uses the mcp_servers parameter in the Messages API.

Authentication

The Vercel AI SDK uses HTTP headers in the transport configuration.
OpenAI uses a headers object in the tool configuration.
Anthropic uses an authorization token parameter.

Tool management

The Vercel AI SDK allows schema discovery and definition at the client level and uses activeTools for filtering.
OpenAI allows tool filtering via allowed_tools parameter.
Anthropic uses a tool configuration object with an allowed_tools array.

Model flexibility

The Vercel AI SDK works with any AI provider (including OpenAI, Anthropic, and Google).
OpenAI only works with OpenAI models.
Anthropic only works with Anthropic models.

Transport options

The Vercel AI SDK uses Streamable HTTP, stdio, or custom transports.
OpenAI uses direct HTTP or HTTPS connections.
Anthropic uses URL-based HTTP connections.

Response handling

The Vercel AI SDK handles responses via unified tool calls and results across all providers.
OpenAI handles responses via the mcp_call and mcp_list_tools items.
Anthropic handles responses via the mcp_tool_use and mcp_tool_result blocks.

Complete example

Here’s a complete example that demonstrates connecting to a Gram MCP server and using it with the Vercel AI SDK:


import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";
 
async function main() {
  // Set up environment variables
  const GRAM_API_KEY = process.env.GRAM_API_KEY;
  const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
 
  if (!GRAM_API_KEY || !OPENAI_API_KEY) {
    throw new Error("Missing required API keys");
  }
 
  let mcpClient;
 
  try {
    // Create HTTP transport with authentication
    const httpTransport = new StreamableHTTPClientTransport(
      new URL("https://app.getgram.ai/mcp/canipushtoprod"),
      {
        headers: {
          Authorization: `Bearer ${GRAM_API_KEY}`,
        },
      },
    );
 
    // Create MCP client with authentication
    mcpClient = await createMCPClient({
      transport: httpTransport,
    });
 
    // Get tools from the MCP server
    const tools = await mcpClient.tools();
    console.log(
      `Connected to MCP server with ${Object.keys(tools).length} tools`,
    );
 
    // Stream a response using the tools
    const result = await streamText({
      model: openai("gpt-4o"),
      tools,
      prompt: "Can I push to production today, and what is the vibe?",
      onStepFinish: ({ toolCalls, toolResults }) => {
        // Log tool usage
        toolCalls?.forEach((call) => {
          console.log(`\n🔧 Called tool: ${call.toolName}`);
        });
 
        toolResults?.forEach((result) => {
          console.log(`📊 Result: ${JSON.stringify(result.output)}`);
        });
      },
    });
 
    // Stream the response to console
    console.log("\n💬 AI Response:");
    for await (const textPart of result.textStream) {
      process.stdout.write(textPart);
    }
    console.log("\n");
  } catch (error) {
    console.error("Error:", error);
  } finally {
    // Always close the MCP client
    if (mcpClient) {
      await mcpClient.close();
      console.log("MCP client closed");
    }
  }
}
 
// Run the example
main().catch(console.error);

Testing your integration

If you encounter issues during integration, follow these steps to troubleshoot:

Validate MCP server connectivity

Before integrating into your application, test your Gram MCP server in the Gram Playground to ensure the tools work correctly.

Use the MCP Inspector

Anthropic provides an MCP Inspector command line tool that helps you test and debug MCP servers before integrating them with the Vercel AI SDK. You can use it to validate your Gram MCP server’s connectivity and functionality.

Run the command below to test your Gram MCP server with the Inspector:


# Install and run the MCP Inspector
npx -y @modelcontextprotocol/inspector

In the Transport Type field, select Streamable HTTP.

Enter your server URL in the URL field, for example:


https://app.getgram.ai/mcp/canipushtoprod

Click Connect to establish a connection to your MCP server.

Screenshot of the MCP Inspector connecting to a Gram MCP server

Use the Inspector to verify that your MCP server responds correctly before integrating it with your Vercel AI SDK application.

Debug tool discovery

You can debug which tools are available from your MCP server:


// Create HTTP transport for Gram MCP server
const httpTransport = new StreamableHTTPClientTransport(
  new URL("https://app.getgram.ai/mcp/canipushtoprod"),
);
 
const mcpClient = await createMCPClient({
  transport: httpTransport,
});
 
const tools = await mcpClient.tools();
 
// List all available tools
console.log("Available tools:");
Object.entries(tools).forEach(([name, tool]) => {
  console.log(`- ${name}: ${tool.description}`);
  console.log(`  Input schema: ${JSON.stringify(tool.inputSchema)}`);
});
 
await mcpClient.close();

Environment setup

Ensure your environment variables are properly configured:


# .env file
OPENAI_API_KEY=your-openai-api-key-here
GRAM_API_KEY=your-gram-api-key-here  # For authenticated servers

Then load them in your application:


import dotenv from "dotenv";
dotenv.config();

What’s next

You now have the Vercel AI SDK connected to your Gram-hosted MCP server, giving your AI applications access to your custom APIs and tools, and giving you the flexibility to use any AI provider.

The Vercel AI SDK’s provider-agnostic approach means you can switch between OpenAI, Anthropic, Google, and other providers while keeping the same MCP tool integration.

Ready to build your own MCP server? Try Gram today and see how easy it is to turn any API into agent-ready tools that work with the Vercel AI SDK and all major AI providers.

Last updated on December 12, 2025