weather-mcp-server

MCP ServerFree

MCP server: weather-mcp-server

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp-compliant weather data exposure via standardized protocol

Medium confidence

Implements the Model Context Protocol (MCP) server specification to expose weather data as callable tools/resources that Claude and other MCP-compatible clients can discover and invoke. Uses MCP's JSON-RPC 2.0 message transport layer with resource and tool definition schemas, enabling seamless integration into agentic workflows without custom API client code.

Solves for

I want Claude to fetch real-time weather data during conversations without leaving the chat interfaceI need to build an AI agent that can check weather conditions as part of a multi-step taskI want to standardize how my AI tools access weather APIs using a protocol-agnostic interface

Best for

AI application developers building Claude-integrated agents

Teams standardizing tool access via MCP across multiple LLM providers

Developers prototyping weather-aware autonomous workflows

Requires

MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM framework)

Network connectivity to upstream weather data provider

MCP server runtime environment (Node.js or Python depending on implementation)

Limitations

Requires MCP client support — not compatible with REST-only API consumers

Weather data freshness depends on underlying provider's update frequency (typically 15-60 minute intervals)

No built-in caching layer — each tool call hits the upstream weather provider

What makes it unique

Implements MCP server specification for weather data, enabling Claude and other MCP clients to discover and call weather tools through standardized protocol rather than custom integrations — abstracts away API client complexity behind MCP's resource/tool schema system

vs alternatives

Provides protocol-standardized weather access vs. custom REST wrappers, allowing drop-in integration with any MCP-compatible LLM client without rewriting integration code

location-based weather data retrieval with coordinate/city name resolution

Medium confidence

Accepts location input (city names, coordinates, or postal codes) and resolves them to weather data by querying an underlying weather provider API. Implements location normalization logic to handle ambiguous inputs (e.g., 'Springfield' → geographic disambiguation) and converts between coordinate systems and human-readable locations.

Solves for

I want to ask Claude 'What's the weather in London?' and get current conditions without specifying coordinatesI need to fetch weather for multiple cities in a batch workflowI want the agent to handle location ambiguity gracefully (e.g., multiple cities with same name)

Best for

Conversational AI applications where users provide natural location names

Multi-location weather aggregation workflows

Applications requiring flexible location input handling

Requires

Underlying weather provider with geocoding/location resolution API

Network access to location resolution service

Limitations

Ambiguous location names may require additional context or user clarification

Geocoding accuracy depends on upstream provider's location database

No support for non-standard location formats (e.g., landmarks, relative directions)

What makes it unique

Abstracts location resolution complexity behind MCP tool interface, allowing Claude to handle natural language location inputs without explicit coordinate specification — implements location normalization and disambiguation at the protocol layer

vs alternatives

Simpler than raw weather API clients because location resolution is built-in and MCP-standardized, vs. requiring separate geocoding library integration

current weather conditions retrieval with unit conversion

Medium confidence

Fetches real-time weather observations (temperature, humidity, wind speed, precipitation, conditions) from upstream provider and converts between unit systems (Celsius/Fahrenheit, m/s/mph/knots) based on client preference. Implements unit conversion logic at the response layer to normalize output regardless of provider's native units.

Solves for

I want to get current weather in Fahrenheit for a US-based userI need weather data in metric units for a European applicationI want the agent to automatically adapt units based on user locale

Best for

Multi-region applications serving users with different unit preferences

International AI agents that need unit-agnostic weather data

Applications requiring precise meteorological measurements

Requires

Upstream weather provider API returning raw meteorological data

Client specification of desired output units

Limitations

Unit conversion adds minimal latency (~5-10ms per request)

Precision may be lost in temperature rounding (e.g., 72.5°F → 22.5°C)

Wind speed conversions require explicit unit specification — no auto-detection from locale

What makes it unique

Implements unit conversion at the MCP tool response layer, allowing clients to request weather in preferred units without managing conversion logic themselves — abstracts unit system complexity from the LLM client

vs alternatives

Cleaner than raw weather API clients because unit conversion is built-in and standardized, vs. requiring client-side conversion logic

weather forecast retrieval with configurable time horizons

Medium confidence

Retrieves multi-day or hourly weather forecasts from upstream provider with configurable forecast length (e.g., 5-day, 10-day, hourly for next 48 hours). Implements time-based filtering and aggregation to return forecast data at requested granularity without requiring client-side parsing of full forecast datasets.

Solves for

I want Claude to tell me the weather forecast for the next 5 daysI need hourly forecast data for the next 48 hours to plan outdoor activitiesI want the agent to fetch only relevant forecast periods (e.g., daytime only)

Best for

Trip planning and activity scheduling agents

Weather-dependent workflow automation (e.g., irrigation scheduling)

Conversational weather assistants providing multi-day outlooks

Requires

Upstream weather provider supporting forecast endpoints

Client specification of forecast length and granularity

Limitations

Forecast accuracy decreases with time horizon (5-day forecasts ~80% accurate, 10-day ~60%)

Hourly forecasts may not be available for all locations from all providers

Forecast data updates on provider schedule (typically 6-12 hour intervals), not real-time

What makes it unique

Exposes forecast data through MCP tool interface with configurable time horizons, allowing Claude to request specific forecast periods without parsing full provider datasets — implements time-based filtering at protocol layer

vs alternatives

More flexible than static forecast endpoints because clients can request custom time horizons and granularity, vs. fixed 5-day or 10-day forecast endpoints

weather alerts and severe weather warnings retrieval

Medium confidence

Fetches active weather alerts, warnings, and advisories (tornado warnings, flood watches, heat advisories, etc.) for specified locations from upstream provider. Implements alert severity classification and filtering to surface critical warnings to LLM clients for inclusion in agent decision-making or user notifications.

Solves for

I want the agent to check for active weather warnings before recommending outdoor activitiesI need to alert users about severe weather in their regionI want to include weather alerts in trip planning logic to avoid dangerous conditions

Best for

Safety-critical applications (travel planning, outdoor activity scheduling)

Emergency response and disaster management agents

Weather-aware notification systems

Requires

Upstream weather provider with alert/warning API

Geographic coverage in region of interest

Limitations

Alert availability varies by region — some areas have comprehensive coverage, others minimal

Alert latency depends on meteorological authority update frequency (typically 15-30 minutes)

No historical alert data — only current/active alerts available

What makes it unique

Integrates severe weather alerts into MCP tool interface, enabling Claude agents to incorporate safety considerations into decision-making — abstracts alert severity classification and filtering from client logic

vs alternatives

Enables safety-aware agent behavior vs. weather-only APIs that ignore alerts, allowing agents to avoid recommending activities during dangerous conditions

air quality and pollution data retrieval

Medium confidence

Fetches air quality index (AQI), pollutant concentrations (PM2.5, PM10, O3, NO2, SO2, CO), and health recommendations from upstream provider. Implements AQI calculation and health impact classification to provide actionable air quality information for health-conscious applications and agents.

Solves for

I want the agent to recommend indoor activities when air quality is poorI need to display AQI and pollutant levels for health-conscious usersI want to include air quality in outdoor activity planning logic

Best for

Health and wellness applications integrating environmental factors

Activity planning agents considering air quality impacts

Environmental monitoring and awareness applications

Requires

Upstream weather provider with air quality API

Air quality monitoring station coverage in region of interest

Limitations

AQI calculation varies by region (US EPA vs. EU vs. China standards differ)

Air quality data may have 1-3 hour latency from measurement to availability

Coverage limited to urban areas with monitoring stations — rural areas often lack data

What makes it unique

Exposes air quality data through MCP tool interface with health impact classification, enabling Claude agents to make health-aware recommendations — abstracts AQI calculation and pollutant interpretation from client logic

vs alternatives

More comprehensive than weather-only APIs because it includes environmental health factors, enabling agents to consider air quality in activity planning

uv index and solar radiation data retrieval

Medium confidence

Fetches UV index, solar radiation intensity, and sun exposure recommendations from upstream provider. Implements UV risk classification (low/moderate/high/very high/extreme) and provides skin protection recommendations based on UV index and skin type considerations.

Solves for

I want the agent to recommend sunscreen and protective measures when UV index is highI need to display UV index and sun exposure risk for outdoor activity planningI want to include solar radiation data in energy generation forecasting

Best for

Health and wellness applications with sun exposure awareness

Outdoor activity planning agents considering UV risk

Solar energy forecasting and renewable energy applications

Requires

Upstream weather provider with UV index and solar radiation API

Limitations

UV index varies significantly with altitude, cloud cover, and time of day

Recommendations are general health guidance, not medical advice

Solar radiation data may not be available from all weather providers

What makes it unique

Integrates UV index and solar radiation into MCP tool interface with health-aware risk classification, enabling Claude agents to provide sun safety recommendations — abstracts UV risk assessment from client logic

vs alternatives

Enables health-aware outdoor activity recommendations vs. weather-only APIs that ignore UV exposure risks

historical weather data retrieval and analysis

Medium confidence

Fetches historical weather observations (temperature, precipitation, wind) for specified date ranges and locations from upstream provider. Implements time-series aggregation and statistical analysis (averages, extremes, trends) to support climate analysis and historical comparison use cases without requiring client-side data processing.

Solves for

I want to compare current weather to historical averages for the same dateI need to analyze weather trends over past months for climate researchI want the agent to provide context about whether current conditions are unusual

Best for

Climate analysis and research applications

Historical weather comparison and anomaly detection

Long-term trend analysis for agriculture and environmental planning

Requires

Upstream weather provider with historical data API

Sufficient historical coverage for requested date range and location

Limitations

Historical data availability varies by location and time period (typically 10-50 years)

Data quality may be inconsistent for older records or remote locations

Statistical analysis requires sufficient data points — sparse historical records limit reliability

What makes it unique

Exposes historical weather data through MCP tool interface with built-in statistical analysis, enabling Claude agents to perform climate analysis without external data processing — abstracts time-series aggregation from client logic

vs alternatives

More analytical than real-time weather APIs because it includes historical context and trend analysis, enabling agents to assess whether current conditions are anomalous

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with weather-mcp-server, ranked by overlap. Discovered automatically through the match graph.

MCP Server23

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weather_mcp

MCP server: weather_mcp

location-based weather service integrationweather data retrieval via mcp

2 shared capabilities

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weather-mcp-server_test

MCP server: weather-mcp-server_test

location-based-weather-query-executionweather-data-retrieval-via-mcp-protocol

2 shared capabilities

MCP Server29

Weather

Get location-based forecasts and real-time US weather alerts. Plan your day with precise, up-to-date conditions at any location. Stay safe with timely warnings for severe weather.

location-based weather forecasting

1 shared capability

MCP Server20

@mcp-monorepo/weather

Weather MCP tools (geocoding, weather-by-coords) for ModelContextProtocol.

weather-query-by-coordinates

1 shared capability

MCP Server22

smithery-weather

MCP server: smithery-weather

real-time weather data retrieval

1 shared capability

Best For

✓AI application developers building Claude-integrated agents
✓Teams standardizing tool access via MCP across multiple LLM providers
✓Developers prototyping weather-aware autonomous workflows
✓Conversational AI applications where users provide natural location names
✓Multi-location weather aggregation workflows
✓Applications requiring flexible location input handling
✓Multi-region applications serving users with different unit preferences
✓International AI agents that need unit-agnostic weather data

Known Limitations

⚠Requires MCP client support — not compatible with REST-only API consumers
⚠Weather data freshness depends on underlying provider's update frequency (typically 15-60 minute intervals)
⚠No built-in caching layer — each tool call hits the upstream weather provider
⚠Ambiguous location names may require additional context or user clarification
⚠Geocoding accuracy depends on upstream provider's location database
⚠No support for non-standard location formats (e.g., landmarks, relative directions)

Requirements

MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM framework)Network connectivity to upstream weather data providerMCP server runtime environment (Node.js or Python depending on implementation)Underlying weather provider with geocoding/location resolution APINetwork access to location resolution serviceUpstream weather provider API returning raw meteorological dataClient specification of desired output unitsUpstream weather provider supporting forecast endpoints

Input / Output

Accepts: structured tool parameters (location, units, forecast type), MCP resource URIs for weather data endpoints, text (city name, e.g., 'London'), coordinates (latitude/longitude), postal codes, location identifier, unit preference parameter (metric/imperial/knots), forecast type (daily/hourly), forecast length (days or hours), optional alert severity filter (critical/moderate/minor), optional pollutant filter (PM2.5, O3, etc.), optional skin type parameter for personalized recommendations, date range (start and end dates), optional analysis type (averages, extremes, trends)

Produces: JSON-structured weather data (temperature, conditions, forecasts), MCP tool result objects with metadata, weather data object with resolved location metadata, geographic coordinates (lat/lon), JSON object with temperature, humidity, wind speed, precipitation in requested units, metadata indicating unit system used, array of forecast objects with timestamp, temperature, conditions, precipitation probability, metadata indicating forecast confidence/accuracy, array of alert objects with type, severity, effective time, expiration, description, empty array if no active alerts, air quality object with AQI value, pollutant concentrations, health recommendations, metadata indicating data freshness and measurement source, UV index object with risk level, solar radiation intensity, protection recommendations, metadata indicating measurement time and forecast validity, array of historical weather objects with date, temperature, precipitation, wind, statistical summary with averages, min/max, trends

UnfragileRank

Adoption5%(25% weight)

Quality16%(25% weight)

Ecosystem52%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

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MCP server: weather-mcp-server

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Capabilities8 decomposed

mcp-compliant weather data exposure via standardized protocol

Medium confidence

Solves for

Best for

AI application developers building Claude-integrated agents

Teams standardizing tool access via MCP across multiple LLM providers

Developers prototyping weather-aware autonomous workflows

Requires

MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM framework)

Network connectivity to upstream weather data provider

MCP server runtime environment (Node.js or Python depending on implementation)

Limitations

Requires MCP client support — not compatible with REST-only API consumers

Weather data freshness depends on underlying provider's update frequency (typically 15-60 minute intervals)

No built-in caching layer — each tool call hits the upstream weather provider

What makes it unique

vs alternatives

Provides protocol-standardized weather access vs. custom REST wrappers, allowing drop-in integration with any MCP-compatible LLM client without rewriting integration code

location-based weather data retrieval with coordinate/city name resolution

Medium confidence

Solves for

Best for

Conversational AI applications where users provide natural location names

Multi-location weather aggregation workflows

Applications requiring flexible location input handling

Requires

Underlying weather provider with geocoding/location resolution API

Network access to location resolution service

Limitations

Ambiguous location names may require additional context or user clarification

Geocoding accuracy depends on upstream provider's location database

No support for non-standard location formats (e.g., landmarks, relative directions)

What makes it unique

vs alternatives

Simpler than raw weather API clients because location resolution is built-in and MCP-standardized, vs. requiring separate geocoding library integration

current weather conditions retrieval with unit conversion

Medium confidence

Solves for

I want to get current weather in Fahrenheit for a US-based userI need weather data in metric units for a European applicationI want the agent to automatically adapt units based on user locale

Best for

Multi-region applications serving users with different unit preferences

International AI agents that need unit-agnostic weather data

Applications requiring precise meteorological measurements

Requires

Upstream weather provider API returning raw meteorological data

Client specification of desired output units

Limitations

Unit conversion adds minimal latency (~5-10ms per request)

Precision may be lost in temperature rounding (e.g., 72.5°F → 22.5°C)

Wind speed conversions require explicit unit specification — no auto-detection from locale

What makes it unique

vs alternatives

Cleaner than raw weather API clients because unit conversion is built-in and standardized, vs. requiring client-side conversion logic

weather forecast retrieval with configurable time horizons

Medium confidence

Solves for

Best for

Trip planning and activity scheduling agents

Weather-dependent workflow automation (e.g., irrigation scheduling)

Conversational weather assistants providing multi-day outlooks

Requires

Upstream weather provider supporting forecast endpoints

Client specification of forecast length and granularity

Limitations

Forecast accuracy decreases with time horizon (5-day forecasts ~80% accurate, 10-day ~60%)

Hourly forecasts may not be available for all locations from all providers

Forecast data updates on provider schedule (typically 6-12 hour intervals), not real-time

What makes it unique

vs alternatives

More flexible than static forecast endpoints because clients can request custom time horizons and granularity, vs. fixed 5-day or 10-day forecast endpoints

weather alerts and severe weather warnings retrieval

Medium confidence

Solves for

Best for

Safety-critical applications (travel planning, outdoor activity scheduling)

Emergency response and disaster management agents

Weather-aware notification systems

Requires

Upstream weather provider with alert/warning API

Geographic coverage in region of interest

Limitations

Alert availability varies by region — some areas have comprehensive coverage, others minimal

Alert latency depends on meteorological authority update frequency (typically 15-30 minutes)

No historical alert data — only current/active alerts available

What makes it unique

vs alternatives

Enables safety-aware agent behavior vs. weather-only APIs that ignore alerts, allowing agents to avoid recommending activities during dangerous conditions

air quality and pollution data retrieval

Medium confidence

Solves for

Best for

Health and wellness applications integrating environmental factors

Activity planning agents considering air quality impacts

Environmental monitoring and awareness applications

Requires

Upstream weather provider with air quality API

Air quality monitoring station coverage in region of interest

Limitations

AQI calculation varies by region (US EPA vs. EU vs. China standards differ)

Air quality data may have 1-3 hour latency from measurement to availability

Coverage limited to urban areas with monitoring stations — rural areas often lack data

What makes it unique

vs alternatives

More comprehensive than weather-only APIs because it includes environmental health factors, enabling agents to consider air quality in activity planning

uv index and solar radiation data retrieval

Medium confidence

Solves for

Best for

Health and wellness applications with sun exposure awareness

Outdoor activity planning agents considering UV risk

Solar energy forecasting and renewable energy applications

Requires

Upstream weather provider with UV index and solar radiation API

Limitations

UV index varies significantly with altitude, cloud cover, and time of day

Recommendations are general health guidance, not medical advice

Solar radiation data may not be available from all weather providers

What makes it unique

vs alternatives

Enables health-aware outdoor activity recommendations vs. weather-only APIs that ignore UV exposure risks

historical weather data retrieval and analysis

Medium confidence

Solves for

Best for

Climate analysis and research applications

Historical weather comparison and anomaly detection

Long-term trend analysis for agriculture and environmental planning

Requires

Upstream weather provider with historical data API

Sufficient historical coverage for requested date range and location

Limitations

Historical data availability varies by location and time period (typically 10-50 years)

Data quality may be inconsistent for older records or remote locations

Statistical analysis requires sufficient data points — sparse historical records limit reliability

What makes it unique

vs alternatives

More analytical than real-time weather APIs because it includes historical context and trend analysis, enabling agents to assess whether current conditions are anomalous

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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weather-mcp-server

Capabilities8 decomposed

mcp-compliant weather data exposure via standardized protocol

location-based weather data retrieval with coordinate/city name resolution

current weather conditions retrieval with unit conversion

weather forecast retrieval with configurable time horizons

weather alerts and severe weather warnings retrieval

air quality and pollution data retrieval

uv index and solar radiation data retrieval

historical weather data retrieval and analysis

Related Artifactssharing capabilities

andy-weather-mcp-server

weather_mcp

weather-mcp-server_test

Weather

@mcp-monorepo/weather

smithery-weather

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

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weather-mcp-server

Capabilities8 decomposed

mcp-compliant weather data exposure via standardized protocol

location-based weather data retrieval with coordinate/city name resolution

current weather conditions retrieval with unit conversion

weather forecast retrieval with configurable time horizons

weather alerts and severe weather warnings retrieval

air quality and pollution data retrieval

uv index and solar radiation data retrieval

historical weather data retrieval and analysis

Related Artifactssharing capabilities

andy-weather-mcp-server

weather_mcp

weather-mcp-server_test

Weather

@mcp-monorepo/weather

smithery-weather

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to weather-mcp-server

Are you the builder of weather-mcp-server?

Get the weekly brief

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