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Blaxel supports “code mode” natively, enabling a more efficient way to execute tool calls over MCP. Essentially, code mode turns any OpenAPI specification into a two-tool MCP server: search lets an AI agent explore the API spec, and execute runs JavaScript code in a sandbox to call the actual API. This tutorial demonstrates by deploying an API as an MCP server on Blaxel and using an agent to work with it. It uses the Claude Agent SDK, but you could also use other frameworks like LangChain, Vercel AI SDK, Mastra, or your own custom code.
For more information on “code mode” or “code execution with MCP”, refer to blog posts by Cloudflare and Anthropic.

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Prerequisites

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1. Install required dependencies

In your project directory, install the Claude Agent SDK for the agent loop and the Blaxel TypeScript SDK / Python SDK for sandbox operations: 
npm init # if new project
npm install @anthropic-ai/claude-agent-sdk @blaxel/core

pnpm init # if new project
pnpm install @anthropic-ai/claude-agent-sdk @blaxel/core

yarn init # if new project
yarn add @anthropic-ai/claude-agent-sdk @blaxel/core

bun init -m --yes # if new project
bun install @anthropic-ai/claude-agent-sdk @blaxel/core

python3 -m venv .venv && source .venv/bin/activate # if new project
pip install claude-agent-sdk blaxel
Export your API keys to the local environment: 
export ANTHROPIC_API_KEY=<ANTHROPIC-API-KEY>
export BL_API_KEY=<BLAXEL-API-KEY>

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2. Build and deploy an MCP server in code mode

This tutorial creates and deploys an MCP server for the example Petstore v2 API. In your project directory, create and run a file named setup.ts (TypeScript) or setup.py (Python) with the following code, or use the Blaxel CLI command shown, to deploy the API on Blaxel. 
import { createFunction } from "@blaxel/core";

async function main() {
  await createFunction({
    body: {
      metadata: {
        name: "petstore-code-mode",
        displayName: "Petstore Code Mode",
      },
      spec: {
        runtime: {
          image: "blaxel/code-mode:latest",
          memory: 2048,
          envs: [
            { name: "OPENAPI_REFERENCE", value: "https://petstore.swagger.io/v2/swagger.json" },
          ],
        },
      },
    },
  });
}

main().catch((err) => {
  console.error(err);
  process.exit(1);
});

import asyncio
import sys

from blaxel.core.client import client
from blaxel.core.client.api.functions.create_function import asyncio as create_function
from blaxel.core.client.models.env import Env
from blaxel.core.client.models.function import Function
from blaxel.core.client.models.function_runtime import FunctionRuntime
from blaxel.core.client.models.function_spec import FunctionSpec
from blaxel.core.client.models.metadata import Metadata


async def main():
    await create_function(
        client=client,
        body=Function(
            metadata=Metadata(
                name="petstore-code-mode",
                display_name="Petstore Code Mode",
            ),
            spec=FunctionSpec(
                runtime=FunctionRuntime(
                    image="blaxel/code-mode:latest",
                    memory=2048,
                    envs=[
                        Env(
                            name="OPENAPI_REFERENCE",
                            value="https://petstore.swagger.io/v2/swagger.json",
                        ),
                    ],
                ),
            ),
        ),
    )


asyncio.run(main())

bl apply -f - <<EOF
apiVersion: blaxel.ai/v1alpha1
kind: Function
metadata:
  displayName: Petstore Code Mode
  name: petstore-code-mode
spec:
  runtime:
    type: mcp
    image: blaxel/code-mode:latest
    memory: 2048
    envs:
      - name: OPENAPI_REFERENCE
        value: https://petstore.swagger.io/v2/swagger.json
EOF
Check the deployment status: 
bl get mcp petstore-code-mode
Once the status shows DEPLOYED, any agent can use petstore-code-mode as a tool provider.

​
3. Build the agent

Obtain the MCP server URL with the following command: 
bl get mcp petstore-code-mode -o json | jq -r '.[] | .metadata.url'
In your project directory, create a file named index.ts (TypeScript) or main.py (Python) with the following code. Replace the placeholder with the MCP server URL from the previous command. 
import { query } from "@anthropic-ai/claude-agent-sdk";

const blApiKey = process.env.BL_API_KEY;
const anthropicApiKey = process.env.ANTHROPIC_API_KEY;
const prompt = process.argv[2];

if (!blApiKey) {
  console.error("Error: BL_API_KEY environment variable is not set");
  process.exit(1);
}

if (!anthropicApiKey) {
  console.error("Error: ANTHROPIC_API_KEY environment variable is not set");
  process.exit(1);
}

if (!prompt) {
  console.error("Error: prompt argument is required");
  process.exit(1);
}

for await (const message of query({
  prompt: prompt,
  options: {
    mcpServers: {
      petstore: {
        type: "http",
        url: "<MCP-SERVER-URL>/mcp",
        headers: { "Authorization": `Bearer ${blApiKey}` },
      },
    },
    allowedTools: ["mcp__petstore__*"],
  },
})) {
  if (message.type === "system" && message.subtype === "init") {
    for (const s of message.mcp_servers) {
      console.log(`MCP server "${s.name}": ${s.status}`);
    }
  }

  if (message.type === "assistant") {
    for (const block of message.message.content) {
      if (block.type === "tool_use") {
        console.log(`\n--- ${block.name} ---`);
        console.log(`  input: ${JSON.stringify(block.input).slice(0, 200)}`);
      }
      if (block.type === "text") {
        console.log(`  text: ${block.text.slice(0, 300)}${block.text.length > 300 ? "…" : ""}`);
      }
    }
  }

  if (message.type === "result" && message.subtype === "success") {
    console.log(`\n${message.result}`);
  }
  if (message.type === "result" && message.subtype !== "success") {
    console.error("Error:", message);
  }
}

import asyncio
import json
import os
import sys

from claude_agent_sdk import (
    query,
    ClaudeAgentOptions,
    SystemMessage,
    AssistantMessage,
    ResultMessage,
    TextBlock,
    ToolUseBlock,
)


bl_api_key = os.environ.get("BL_API_KEY")
anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY")
prompt = sys.argv[1] if len(sys.argv) > 1 else None

if not bl_api_key:
    print("Error: BL_API_KEY environment variable is not set", file=sys.stderr)
    sys.exit(1)

if not anthropic_api_key:
    print("Error: ANTHROPIC_API_KEY environment variable is not set", file=sys.stderr)
    sys.exit(1)

if not prompt:
    print("Error: prompt argument is required", file=sys.stderr)
    sys.exit(1)


async def main():
    async for message in query(
        prompt=prompt,
        options=ClaudeAgentOptions(
            mcp_servers={
                "petstore": {
                    "type": "http",
                    "url": "<MCP-SERVER-URL>/mcp",
                    "headers": {"Authorization": f"Bearer {bl_api_key}"},
                }
            },
            allowed_tools=["mcp__petstore__*"],
        ),
    ):
        if isinstance(message, SystemMessage) and message.subtype == "init":
            for s in message.data.get("mcp_servers", []):
                print(f'MCP server "{s["name"]}": {s["status"]}')

        if isinstance(message, AssistantMessage):
            for block in message.content:
                if isinstance(block, ToolUseBlock):
                    print(f"\n--- {block.name} ---")
                    print(f"  input: {json.dumps(block.input)[:200]}")
                if isinstance(block, TextBlock):
                    text = block.text
                    print(f"  text: {text[:300]}{'…' if len(text) > 300 else ''}")

        if isinstance(message, ResultMessage) and message.subtype == "success":
            print(f"\n{message.result}")
        if isinstance(message, ResultMessage) and message.subtype != "success":
            print("Error:", message, file=sys.stderr)


asyncio.run(main())

​
4. Test the agent

Send the agent a query: 
tsx index.ts "list all available pets"

bun index.ts "list all available pets"

python main.py "list all available pets"
You should see the agent using the two available tools, first searching for the correct API endpoint and then writing and executing code to query the API and respond to your prompt. In case of errors, you can view the logs of the deployed MCP server with the command below: 
bl logs mcp petstore-code-mode
The following resources will help you go further:

Build and deploy an agent on Blaxel with Claude Agent SDK

Complete tutorial for building an agent with Claude Agent SDK and deploying it on Blaxel as a serverless auto-scalable API.

Give compute to your agent with the TypeScript SDK

Complete tutorial for using the TypeScript SDK to develop an agent using Blaxel services.

Give compute to your agent with the Python SDK

Complete tutorial for using the Python SDK to develop an agent using Blaxel services.

Deploy your agent code to Blaxel

Complete tutorial for deploying AI agents to Blaxel.
Last modified on May 18, 2026