How to Use the Tomorrow.io Plus MCP in LlamaIndex

Q: Can I search past Tomorrow.io Plus alerts using LlamaIndex?

Yes, you pull alert lists via listalerts. Since the output is indexed, you can run semantic searches across previous sessions of weather warnings and insights.

Q: What data points can I index for Tomorrow.io Plus with LlamaIndex?

You can pull structured forecasts using getforecastweather or historical records via getrecenthistoryweather. Indexing these time-series datasets makes them available for advanced retrieval.

Q: Is the MCP Server useful for complex data structures with LlamaIndex?

Yes. The posttimelines tool handles GeoJSON geometries, and indexing this output means your RAG application can answer questions about weather patterns across specific, defined areas.

Index live weather API data into a searchable knowledge base using LlamaIndex.

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Connect Tomorrow.io Plus MCP to LlamaIndex

Create your Vinkius account to connect Tomorrow.io Plus 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 Plus with LlamaIndex

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Indexing Alerts and Events in LlamaIndex

Don't just call the tools—index them. You can run `list_alerts` or `list_events` to pull structured warnings. These outputs become part of your searchable knowledge base. When a user asks about 'recent severe weather,' the index retrieves both documents and these live API findings, grounding the answer in actual data.

Retrieving Forecast Data for RAG Applications

Forecasting is key. Use `get_forecast_weather` to pull daily or hourly projections. LlamaIndex then indexes this time-series data alongside your operational documents. This lets you query historical weather reports and combine them with internal manuals, giving answers that are both contextual and factually accurate.

Querying Location Metadata via MCP Server

Need context about where the data comes from? Use `list_locations` or `list_insights`. These tools pull metadata defining available areas and insight categories. By indexing this structure, your LlamaIndex RAG application can answer questions like, 'What insight categories are available for the Denver area?'

Setup guide

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

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Common questions about Tomorrow.io Plus MCP in LlamaIndex

The best way is to let the MCP tool output become part of your vector store index. You can run `get_realtime_weather` and then query that live result alongside your internal company policies.

Yes, you pull alert lists via `list_alerts`. Since the output is indexed, you can run semantic searches across previous sessions of weather warnings and insights.

You can pull structured forecasts using `get_forecast_weather` or historical records via `get_recent_history_weather`. Indexing these time-series datasets makes them available for advanced retrieval.

Yes. The `post_timelines` tool handles GeoJSON geometries, and indexing this output means your RAG application can answer questions about weather patterns across specific, defined areas.

This server touches structured weather data, including current conditions and historical records spanning temperature and precipitation intensity. All tool calls are processed through Vinkius's secure endpoint token.

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