Tomorrow.io MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tomorrow.io as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Tomorrow.io. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Tomorrow.io?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Tomorrow.io MCP Server
Connect your Tomorrow.io account to any AI agent and integrate institutional-grade weather modeling into your logic flows. Retrieve hyperlocal conditions, predict rainfall down to the specific minute, and access specialized environmental matrices (air quality, fire risks, and ground road weather) directly through natural language queries.
LlamaIndex agents combine Tomorrow.io tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Real-time Observations — Check comprehensive atmospheric indicators for any latitude, longitude, city, or zip code dynamically
- Interval Forecasting — Read forward-looking timelines segmented by minute (precipitation), hours (daily events), or deep daily projections up to 15 days out
- Environmental Hazards — Interrogate the AQI (Air Quality Index), pollen density predictions, or active Wildfire index algorithms
- Logistical Safeguards — Check specialized
Road Riskparameters natively, enabling safer fleet routing algorithms against complex weather patterns - Historical Auditing — Query observed historical conditions by defining past temporal boundaries and desired weather field sets for retroactive analysis
The Tomorrow.io MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Tomorrow.io to LlamaIndex via MCP
Follow these steps to integrate the Tomorrow.io MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Tomorrow.io
Why Use LlamaIndex with the Tomorrow.io MCP Server
LlamaIndex provides unique advantages when paired with Tomorrow.io through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tomorrow.io tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tomorrow.io tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tomorrow.io, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tomorrow.io tools were called, what data was returned, and how it influenced the final answer
Tomorrow.io + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tomorrow.io MCP Server delivers measurable value.
Hybrid search: combine Tomorrow.io real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tomorrow.io to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Tomorrow.io for fresh data
Analytical workflows: chain Tomorrow.io queries with LlamaIndex's data connectors to build multi-source analytical reports
Tomorrow.io MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Tomorrow.io to LlamaIndex via MCP:
get_air_quality_index
Retrieve current and forecast air quality data
get_custom_timelines
Query weather data for custom time ranges and arbitrary intervals
get_daily_forecast
Returns up to 15 days of daily intervals. Retrieve daily weather forecast extremes and totals
get_historical_weather
Retrieve actual recorded historical weather observations
get_hourly_forecast
Returns up to 120 hours of predictions. Retrieve hour-by-hour weather forecast for a location
get_minutely_precipitation
Retrieve minute-by-minute precipitation nowcast
get_pollen_forecast
Retrieve daily pollen count indices
get_realtime_weather
Provide a location (lat,lon, city name, or zip) and field list. Retrieve current real-time weather conditions for any global location
get_road_weather_risk
Retrieve assessments for driving and road hazards
get_wildfire_risk
Retrieve wildfire risk index and weather conditions
Example Prompts for Tomorrow.io in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tomorrow.io immediately.
"What is the expected air quality index in New York over the next hour?"
"Show me the minute-by-minute precipitation near Golden Gate bridge right now."
Troubleshooting Tomorrow.io MCP Server with LlamaIndex
Common issues when connecting Tomorrow.io to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTomorrow.io + LlamaIndex FAQ
Common questions about integrating Tomorrow.io MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Tomorrow.io with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Tomorrow.io to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.