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weather-mcp-server

by bhujangarao-db
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Python
Remote

Weather MCP Server

A Python Model Context Protocol server that gives an AI current weather and forecasts. It geocodes locations and reads forecast data from Open-Meteo, then optionally uses Groq to explain the result naturally.

Requirements

  • Python 3.11 or newer

  • A Groq API key for AI-generated summaries

  • Internet access for Open-Meteo and Groq requests

Related MCP server: Weather MCP Server

Setup

PowerShell:

py -3.11 -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
Copy-Item .env.example .env

Open .env and set GROQ_API_KEY. The server still works without the key, but it returns structured JSON instead of an AI summary.

Run

Browser UI

Start the user-facing weather chat:

python -m weather_mcp_server.web

Then open http://127.0.0.1:8000. Ask questions such as “Will it rain in London tomorrow?” The browser sends the question to the web adapter, which invokes the same get_weather tool exposed by the MCP server.

Run the server directly:

python -m weather_mcp_server.server

MCP clients can use the checked-in .vscode/mcp.json configuration. In VS Code, open this folder as the workspace, install the Python extension if prompted, and start the weather-mcp-server MCP server from the MCP controls.

The server exposes one tool:

  • get_weather(location, forecast_days=3): returns current conditions and 1-7 days of forecast data.

Test

python -m pytest

The tests mock Open-Meteo, so they do not require network access or a Groq key.

Available Tools

1 tool
get_weatherA

Get current conditions and a weather forecast for a place.

Args: location: A city, region, or country name, such as "London" or "Tokyo". forecast_days: Number of forecast days to return, from 1 through 7.

ParametersJSON Schema
NameRequiredDescriptionDefault
locationYes
forecast_daysNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden of behavioral disclosure. It conveys the read-only nature of the operation and the high-level output (current conditions and forecast), but it does not mention units, error handling, or what the forecast includes, leaving some behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a two-item Args list. The main purpose is front-loaded, and every line adds either scope or parameter detail, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only two parameters, one of which has a default, and an output schema present, the description covers the necessary input semantics and high-level behavior. Gaps like unit conventions are minor given the output schema, so the description is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, yet the description's Args section fully compensates: it defines 'location' as a city, region, or country with examples, and 'forecast_days' as a range from 1 through 7. This adds meaningful semantics beyond the bare schema properties.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Get current conditions and a weather forecast for a place,' which names a specific action (get) and resource (conditions + forecast). This clearly distinguishes the tool's function even in the absence of siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly communicates that the tool is for retrieving weather data for a location, and the Args section explains how to supply the location and optional forecast length. Since there are no sibling tools, explicit when-not or alternative guidance is unnecessary; the implied usage context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedget_weather
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is no possibility of confusing it with another. The tool's purpose is clearly defined and distinct.

Naming Consistency5/5

The single tool uses a clear, conventional verb_noun pattern (get_weather), which is consistent and intuitive. There are no naming inconsistencies to evaluate.

Tool Count3/5

A single tool for a weather server is minimal but borderline acceptable because it combines current conditions and forecast. However, it feels thin compared to the typical scope of a weather-focused MCP server.

Completeness4/5

The tool covers the core weather needs—current conditions and forecast—with configurable forecast days. Missing features like alerts or historical data are minor gaps that agents can work around for basic weather queries.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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