How to Use the NOAA Climate — Historical Weather Records MCP in Google ADK

Q: How do I configure NOAA Climate — Historical Weather Records with Google ADK?

Install google-adk via pip. Initialize an McpToolset using StreamableHttpServerParameters with your endpoint URL. Pass that toolset into your LlmAgent constructor under the tools parameter.

Q: Can Google ADK filter the NOAA tools exposed to the agent?

Yes. You can use the toolnames filter in the McpToolset setup. If you only want the agent to access getclimatenormals and getyearlysummary, you simply restrict the list.

Q: How does NOAA Climate — Historical Weather Records handle Gemini's context window?

Gemini's 1M+ token limit is perfect for the getdailydata tool. The agent can pull massive arrays of daily TMAX and TMIN values and analyze the entire historical block without chunking the context.

Q: What data does the station search return in this MCP Server?

The searchstations tool returns specific NCEI station IDs, formal names, and geographic coordinates. Your agent uses these IDs as required inputs for the other summary and daily data tools.

Feed decades of NOAA weather data into your Google ADK agents for deep climate reasoning.

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Connect NOAA Climate — Historical Weather Records MCP to Google ADK

Create your Vinkius account to connect NOAA Climate — Historical Weather Records to Google ADK 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 NOAA Climate — Historical Weather Records with Google ADK

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Process decades of weather with Google ADK

Gemini's massive context window is built for heavy datasets. When your agent calls `get_daily_data`, it can ingest years of raw GHCN-Daily temperature and precipitation records in a single pass. The Google ADK agent holds that entire timeline in memory. It cross-references those daily spikes against the 30-year baseline pulled from `get_climate_normals`, letting it spot micro-anomalies that standard statistical models miss.

Map global NCEI stations

You cannot analyze a region without knowing where the sensors are. The `search_stations` tool lets your agent query NCEI hardware by location bounding box or keyword. The agent retrieves the station IDs and exact coordinates. It can then pipe those locations directly into your existing BigQuery geospatial datasets for further enterprise mapping.

Aggregate long-term climate shifts via MCP

Short-term volatility distracts from macro trends. Your agent uses `get_yearly_summary` to extract annual temperature averages and extreme values over multiple decades. For tighter resolution, the agent switches to `get_monthly_summary`. This MCP Server feeds those GSOM records straight into Vertex AI, giving your enterprise models the exact heating degree days required to calculate long-term infrastructure risk.

Setup guide

Set up NOAA Climate — Historical Weather Records MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with NOAA Climate — Historical Weather Records tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="NOAA Climate — Historical Weather Records_agent",
    model="gemini-2.0-flash",
    instruction="You have access to NOAA Climate — Historical Weather Records tools via MCP.",
    tools=mcp_tools,
)

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Common questions about NOAA Climate — Historical Weather Records MCP in Google ADK

Install `google-adk` via pip. Initialize an `McpToolset` using `StreamableHttpServerParameters` with your endpoint URL. Pass that toolset into your `LlmAgent` constructor under the `tools` parameter.

Yes. You can use the `tool_names` filter in the `McpToolset` setup. If you only want the agent to access `get_climate_normals` and `get_yearly_summary`, you simply restrict the list.

Gemini's 1M+ token limit is perfect for the `get_daily_data` tool. The agent can pull massive arrays of daily TMAX and TMIN values and analyze the entire historical block without chunking the context.

The `search_stations` tool returns specific NCEI station IDs, formal names, and geographic coordinates. Your agent uses these IDs as required inputs for the other summary and daily data tools.

The server only pulls public climate variables like snow depth (SNWD) and heating degree days. Your infrastructure queries a read-only endpoint. Zero proprietary enterprise data from your Google Cloud environment is transmitted to the weather archives.

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