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Beyond live search, Grok’s Responses API offers two more server-side tools: code execution (code_interpreter, a server-side Python sandbox) and Remote MCP (xAI’s servers connect directly to an MCP server you specify). Both are verified working on APIYI (July 13, 2026, UTC+8) with a default-group key.

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Code Execution

The model writes Python and actually runs it in xAI’s server-side sandbox — ideal for exact computation and data processing. In testing we asked for 2 to the power of 100: the model executed print(2 ** 100) and returned the exact value (big-integer math that pure language models routinely get wrong; code execution is exact): 
from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key",
    base_url="https://api.apiyi.com/v1",
)

resp = client.responses.create(
    model="grok-4.5",
    tools=[{"type": "code_interpreter"}],
    input="Compute 2 to the power of 100 exactly, using code",
)
print(resp.output_text)
# 2^100 = 1267650600228229401496703205376

# Inspect the code the model actually ran
for item in resp.output:
    if item.type == "code_interpreter_call":
        print("Executed code:", item.code)
Measured latency for a single code-execution task: ~6 s. usage.server_side_tool_usage_details.code_interpreter_calls records the execution count.

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Remote MCP Tools

Declare an external MCP server in the request and xAI’s servers automatically connect, list its tools, and call them as needed — no local MCP client required. Verified connecting to a public MCP server and completing a tool call (~16 s): 
resp = client.responses.create(
    model="grok-4.5",
    tools=[{
        "type": "mcp",
        "server_label": "deepwiki",
        "server_url": "https://mcp.deepwiki.com/mcp",
        "require_approval": "never"
    }],
    input="Use deepwiki to find out what the openai/openai-python repo does, in one sentence",
)
print(resp.output_text)

# Inspect MCP call details
for item in resp.output:
    if item.type == "mcp_call":
        print("Tool:", item.name, "| Output:", item.output[:200])
ParameterNotes
server_labelYour label for distinguishing multiple MCP servers
server_urlMCP server address (must be publicly reachable — xAI’s servers connect directly)
require_approval"never" calls tools automatically; the default returns approval requests requiring a second round
  • The MCP server must be publicly reachable: connections originate from xAI’s servers; intranet / localhost addresses won’t work.
  • Mind data security: your conversation content is sent through xAI’s servers to that MCP server — only connect services you trust.
  • External server availability is outside APIYI’s control; on failures, check the server’s status first.

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Collections Search (RAG) — Not Available

xAI also offers a collections_search (knowledge-base retrieval / file_search) tool. It is not usable on APIYI: it requires files uploaded and collections built in the xAI console beforehand, and APIYI runs in key-pool mode with no upstream console access. In testing the request passes through but retrieval inevitably fails (file_search_call returns failed). For RAG, build retrieval on your side (vector store + inject recalled content into the prompt), leveraging Grok’s 1M context and automatic caching.

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FAQ

Yes. The tools array can include web_search / x_search / code_interpreter / mcp together; the model decides which to call per task. usage.server_side_tool_usage_details counts each separately.
The sandbox targets computation (Python math / data processing) and cannot access your local files. For external data, combine it with web_search or MCP tools.
Model-side reasoning tokens are billed normally. Tool call fees follow APIYI tool pricing and your actual billing statement — validate with small volumes before scaling up.

Web & X Search

The live-search tools on the same Responses API

Grok Overview

Model lineup, pricing, and the full capability-boundary table