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nagesh-db

Weather MCP Server

by nagesh-db
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
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

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the disclosure burden. It states the tool returns current conditions and a forecast, which is useful, but it does not explain units, timezone behavior, error handling, or whether the data is real-time or cached. For a simple read-only tool this is moderate transparency, but not complete.

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

Conciseness4/5

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

The description is short and informative, leading with the main purpose and then documenting parameters. It is concise but not overly sparse, though the parameter details are somewhat redundant with what a good schema could provide.

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?

For a simple two-parameter weather tool with an output schema, the description covers the core functionality and parameter semantics well. It does not specify forecast units, date formats, or error behavior, but these are less critical given the simplicity of the tool and the presence of an output schema.

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?

The schema has 0% description coverage, and the description fully compensates by explaining both parameters: location expects a city, region, or country name, and forecast_days specifies the number of forecast days (1-7). This added meaning goes well beyond the raw parameter names and types.

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 clearly states the tool retrieves current weather conditions and a forecast for a location, with a specific verb and resource. The tool name and description align, and there are no sibling tools to confuse it with.

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

Usage Guidelines3/5

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

The description gives a clear sense of when to use the tool—whenever weather data is needed—but provides no explicit guidance on alternatives or exclusion criteria. Since there are no sibling tools, some implied usage is acceptable, but the description could still clarify typical use cases or limitations.

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. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedget_weather
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TDQS

A4.1/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools. The single tool has a clear, unique purpose.

Naming Consistency5/5

The tool name follows the standard verb_noun pattern (get_weather), which is clear and predictable. With only one tool, consistency is trivially maintained.

Tool Count3/5

A single tool feels thin for a weather-focused server, even though get_weather covers both current conditions and forecasts. The server is on the low end of acceptable scope.

Completeness4/5

The tool covers the core weather use cases: current conditions and multi-day forecasts. Obvious gaps like historical data or weather alerts exist, but the essential surface is present.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

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  • Weather MCP Server

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  • OpenWeather MCP Server

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  • Weather MCP Server

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    Enables AI assistants to retrieve current weather, forecasts, and summaries for any global location using the Open-Meteo API, with no API key required.
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    Creative Commons Zero v1.0 Universal

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