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MCP Weather Server

by microsoft
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

MCP Weather Server

A simple Model Context Protocol (MCP) server that provides real-time weather data to AI agents like GitHub Copilot.

Quick Start

Clone the repository:

git clone https://github.com/microsoft/lets-learn-mcp-javascript.git
cd mcp-weather-server

1. Install Dependencies

npm install

2. Run the Server

Test with MCP Inspector:

npx -y @modelcontextprotocol/inspector npx -y tsx main.ts

3. Use with VS Code

  1. Open the mcp.jsonfile in .vscode folder

  2. Click the start server button above line 4

  3. Open Chat mode and select agent and choose a modal that supports MCPs such as Claude Sonnet

  4. Type or speak into the chat and ask it what the weather is like in your city

Related MCP server: MCP Weather Server

Features

  • 🌤️ Real-time weather data for any city

  • 🌍 No API key required (uses Open-Meteo)

  • 🤖 Works with GitHub Copilot and other MCP-compatible AI tools

  • ⚡ Easy to test with MCP Inspector

Usage Examples

Ask GitHub Copilot:

  • "What's the weather like in Tokyo?"

  • "How's the weather in London today?"

  • "Give me the current weather for Paris"

How It Works

The server provides a get-weather tool that:

  1. Converts city names to coordinates using geocoding

  2. Fetches current weather data from Open-Meteo API

  3. Returns structured data that AI agents can format beautifully

Code Structure

// Creates MCP server with weather tool
const server = new McpServer({
  name: "Weather Server",
  version: "1.0.0"
});

// Defines the get-weather tool
server.tool('get-weather', 'Tool to get the weather of a city', ...);

// Connects via stdio transport
const transport = new StdioServerTransport();
server.connect(transport);

Dependencies

  • @modelcontextprotocol/sdk - MCP server framework

  • zod - Schema validation

  • tsx - TypeScript execution (for development)

API Used

  • Open-Meteo - Free weather API with no authentication required

Available Tools

2 tools
get_alertsC

Get weather alerts for a state

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYesTwo-letter state code (e.g. CA, NY)

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It states what the tool does but doesn't cover aspects like whether it's read-only, requires authentication, has rate limits, or what the output format might be, which are critical for a tool with no output schema.

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 a single, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core function without unnecessary elaboration.

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

Completeness2/5

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

Given the tool's simplicity (one parameter, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output expectations, and usage context relative to the sibling tool, making it insufficient for full agent understanding despite the straightforward schema.

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

Parameters3/5

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

The description mentions 'for a state', which aligns with the single parameter 'state' in the input schema. Since schema description coverage is 100% (the schema fully documents the parameter as a two-letter state code), the description adds little beyond what the schema provides, meeting the baseline score.

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

Purpose4/5

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('weather alerts for a state'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather data, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'get_forecast' or specify scenarios where alerts are preferred over forecasts, leaving usage context implied at best.

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

get_forecastC

Get weather forecast for a location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYesLatitude of the location
longitudeYesLongitude of the location

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It states the action ('Get weather forecast') but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or response format. This is inadequate for a tool that likely interacts with external data sources.

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 extremely concise—a single sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the essential information, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions.

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

Completeness2/5

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

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the forecast includes (e.g., temperature, precipitation), the time range covered, or how results are structured. Given the complexity of weather data and lack of structured output, more context is needed for effective use.

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

Parameters3/5

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

The input schema has 100% description coverage, with clear documentation for both parameters (latitude and longitude), including valid ranges. The description doesn't add any semantic details beyond what the schema provides, such as examples of locations or coordinate systems. Given the high schema coverage, a baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('weather forecast for a location'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from its sibling 'get_alerts', which might also relate to weather information, leaving room for potential confusion about when to use each.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its sibling 'get_alerts' or any alternatives. It lacks context about specific scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and basic purpose.

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. 2 tool updates
    • First observedget_alerts
    • First observedget_forecast
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TDQS

B3.1/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for a state, while get_forecast provides weather forecasts for a location. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for alerts versus forecasts.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). This uniformity enhances readability and predictability, making the tool set easy to navigate and understand.

Tool Count2/5

With only 2 tools, the server feels under-scoped for a weather domain, which typically involves more operations like current conditions, historical data, or radar information. This limited set may restrict agent capabilities in handling comprehensive weather-related tasks.

Completeness2/5

The tool surface is significantly incomplete for a weather server, lacking essential operations such as getting current conditions, historical weather data, or radar imagery. This creates notable gaps that could lead to agent failures when trying to perform common weather-related workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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