How to Use the Tomorrow.io Extended MCP in LlamaIndex

Q: Can I query past data from Tomorrow.io Extended and LlamaIndex?

Yes. You build a unified index that accepts API data—like getrecenthistory results—and combines it with your own documents. This lets you query historical conditions instantly.

Q: Is Tomorrow.io Extended suitable for long-term planning using LlamaIndex?

It is. By indexing the getweatherforecast results, you can build models that answer questions like, 'Based on the last five forecast cycles, what's our average peak temperature?'

Q: How do I ensure data access when using Tomorrow.io Extended and LlamaIndex?

The client supports includeresources=True. This lets you explicitly tell the system to treat the API data—like air quality readings—as part of the indexable resource set.

Ground your AI answers with Tomorrow.io Extended and LlamaIndex.

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Connect Tomorrow.io Extended MCP to LlamaIndex

Create your Vinkius account to connect Tomorrow.io Extended to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Tomorrow.io Extended with LlamaIndex

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Indexing MCP Server data with LlamaIndex.

When you run a tool, like `get_air_quality`, LlamaIndex doesn't just spit out the result; it indexes that raw data. This means you can query past air quality reports weeks later and get answers grounded in actual API readings. The output from any MCP Server tool becomes part of your searchable knowledge base. You build RAG applications where live weather data merges with static documents.

Creating a unified index for Tomorrow.io Extended.

LlamaIndex lets you combine the results from `get_recent_history` and official reports into one queryable index. You stop getting vague answers and start getting data points verified against real API history. This process makes your AI client much more trustworthy because it always cites specific weather records.

Searching alerts across multiple MCP Server tools.

You can query the index to ask, 'What were the highest wind warnings last quarter?' LlamaIndex searches through all recorded `list_weather_alerts` data. The ability to filter and include resources means you're not limited just to what was returned in a single API call.

Setup guide

Set up Tomorrow.io Extended MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Tomorrow.io Extended MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Tomorrow.io Extended tools.",
)
response = await agent.run("List recent Tomorrow.io Extended data")

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Common questions about Tomorrow.io Extended MCP in LlamaIndex

LlamaIndex turns the tool output into searchable vectors, not just text. When you query it, it retrieves semantically related data from past API runs. You get answers based on indexed facts, eliminating hallucinations.

This MCP Server touches air quality, weather forecasts, and alerts. When using it with LlamaIndex, the specific metrics (e.g., pollutant levels, temperature ranges) are indexed into your knowledge base.

Yes. You build a unified index that accepts API data—like `get_recent_history` results—and combines it with your own documents. This lets you query historical conditions instantly.

It is. By indexing the `get_weather_forecast` results, you can build models that answer questions like, 'Based on the last five forecast cycles, what's our average peak temperature?'

The client supports `include_resources=True`. This lets you explicitly tell the system to treat the API data—like air quality readings—as part of the indexable resource set.

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