How to Use the Tomorrow.io MCP in LlamaIndex

Q: Can LlamaIndex use Tomorrow.io for historical context?

Yes. By indexing the output of gethistoricalweather, you allow your agent to retrieve factual weather records and answer questions grounded in real-world data, eliminating hallucinations.

Q: Does Tomorrow.io support risk assessment knowledge for LlamaIndex?

It does. You can index the results from both getroadweatherrisk and getwildfirerisk. This lets your agent answer, 'Given these two risks, what's the safest route?'

Ground your knowledge with Tomorrow.io data using LlamaIndex.

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Connect Tomorrow.io MCP to LlamaIndex

Create your Vinkius account to connect Tomorrow.io 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 with LlamaIndex

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Indexing Weather History

The `get_historical_weather` tool retrieves actual recorded observations, which you can index into a vector store. This means your knowledge base doesn't just hold documents; it holds facts. Later, you query this data to ground an answer—for example, 'What was the average rainfall in Boston during the last hurricane?'

Searchable Forecasts

You can index forecasts using `get_daily_forecast` or `get_hourly_forecast`. When a user asks about future conditions, LlamaIndex pulls the relevant forecast data and answers directly from that indexed information. This makes your RAG app far more reliable than relying on general knowledge.

Real-Time Data Knowledge Base

Using `get_realtime_weather` populates a searchable index with current conditions for any global location. This is perfect for building an operational dashboard that answers, 'What's the current air quality in Seattle?' It lets your agent query immediate data alongside long-term documents.

Setup guide

Set up Tomorrow.io 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 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 tools.",
)
response = await agent.run("List recent Tomorrow.io data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Tomorrow.io. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Tomorrow.io MCP in LlamaIndex

The server's tool outputs become indexed knowledge. Instead of just calling `get_air_quality_index`, you index the result, allowing your agent to answer complex questions like, 'Based on past air quality data and current pollen levels, what should I bring?'

It provides everything from minute-by-minute precipitation (`get_minutely_precipitation`) to daily pollen counts via `get_pollen_forecast`. You can combine these disparate data points into a single, queryable index.

Yes. By indexing the output of `get_historical_weather`, you allow your agent to retrieve factual weather records and answer questions grounded in real-world data, eliminating hallucinations.

It does. You can index the results from both `get_road_weather_risk` and `get_wildfire_risk`. This lets your agent answer, 'Given these two risks, what's the safest route?'

The server provides highly structured environmental data, including air quality indices and precipitation levels, which are ideal for creating dense, factual vector embeddings.

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