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Documentation Index

Fetch the complete documentation index at: https://docs.bigdata.com/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

This guide shows how to connect the Grok SDK to the Bigdata MCP server so that a Grok-powered agent can search for financial news, resolve securities, and generate structured analysis using Bigdata.com. You will configure a remote MCP tool inside a Grok API call, point it at the Bigdata MCP server, and restrict the tool set to exactly the tools you need.

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Requirements

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Step 1: Install the xAI SDK

The xAI SDK supports multiple languages. This guide uses Python, but you can also use cURL or any HTTP client to call the API directly. Follow the official xAI SDK installation guide for the full reference. 
pip install xai-sdk

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Step 2: Create a Bigdata API key

You can create your API Key in the Developer Platform > API Keys. Developer Platform > API Keys

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Step 3: Connect Grok to Bigdata MCP

The Grok SDK supports Remote MCP tools natively. You can connect to the Bigdata MCP server by adding an MCP tool definition to your API call. By default, Grok discovers all tools exposed by the MCP server. To restrict the agent to a subset, pass the allowed_tool_names parameter. In this example we enable two tools:
grok_bigdata_agent.py
import os

from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import mcp

client = Client(api_key=os.getenv("XAI_API_KEY"))

response = client.chat.create(
    model="grok-4.3",
    tools=[
        mcp(
            server_url="https://mcp.bigdata.com/",
            extra_headers={"x-api-key": os.getenv("BIGDATA_API_KEY")},
            allowed_tool_names=["bigdata_search", "find_securities"],
        ),
    ],
)

response.append(user("What are the top financial news topics today that could impact NVIDIA?"))
# Send the conversation to Grok and get the response
result = response.sample()

# Print the tools that were called and their arguments
for tool_call in result.tool_calls:
    print(f"Tool: {tool_call.function.name}")
    print(f"  Arguments: {tool_call.function.arguments}")
    print()

# Print the response text
print(result.content)

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Step 4: Run and review the results

You can now run your script with the following command 
python grok_bigdata_agent.py
Grok will call the Bigdata MCP tools during its reasoning loop, search for relevant financial content, and return a response grounded in real-time data from Bigdata.com. Output: Grok results

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Enabling additional tools

The Bigdata MCP server exposes several tools beyond bigdata_search and find_securities. For example, you can add bigdata_company_tearsheet for comprehensive company profiles or bigdata_sentiment_tearsheet for real-time media sentiment analysis. Simply add the tool names to the allowed_tool_names list (or allowed_tools in cURL). To enable all tools, remove the parameter entirely and Grok will discover them automatically. For the complete list of available tools, see the Bigdata MCP tools overview.

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Tips and customization

Avoid hardcoding API keys in your scripts. Store them in environment variables or a .env file:
export XAI_API_KEY="your-grok-api-key"
export BIGDATA_API_KEY="your-bigdata-api-key"
The code example in Step 3 already reads these via os.getenv().
Grok supports multiple MCP servers in a single request. Add additional tool objects to the tools array to connect other data sources alongside Bigdata.
Replace grok-4.3 with any available Grok model. See the xAI models page for the latest recommendations, or browse the full model list in the xAI console.