Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agrisignal-mcp Should I irrigate my field today?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server for farm and land decision support, built entirely on free, keyless public data: Open-Meteo (weather, soil moisture/temperature, evapotranspiration) and SoilGrids (ISRIC's global 250m-resolution soil map). No API keys, no signup, no OAuth — every tool works out of the box.
Unlike general-purpose weather/soil API wrappers, this server doesn't just hand back raw data — it synthesizes weather and soil into farming decisions: whether to irrigate and how much, frost and heat stress risk windows, growing degree day accumulation for predicting crop development, and drought tracking. The goal is to be useful to someone without access to expensive precision-agriculture services — a smallholder farmer, an agronomy student, an extension worker, an NGO.
Every recommendation shows its underlying numbers and is framed as a data-grounded estimate, not certified agronomic advice. These are simplified models (see each tool's caveats below) meant for triage and planning, not a substitute for site-specific expertise.
Tool | Description |
| Resolve a place name to coordinates (required first step for the others) |
| Soil texture, pH, organic carbon at multiple depths (SoilGrids) |
| Current weather + soil snapshot + short forecast |
| Soil water balance estimate: should you irrigate, and how much? |
| Scans the forecast for frost/freeze and heat-stress risk days |
| GDD accumulation over a date range (crop development/maturity tracking) |
| Consecutive dry days and total precipitation — a rough drought indicator |
Related MCP server: Weather MCP Server
No API keys needed. Add it to your MCP client and go.
Claude Code:
claude mcp add --transport stdio agrisignal -- npx -y agrisignal-mcpClaude Desktop — add to claude_desktop_config.json:
{
"mcpServers": {
"agrisignal": {
"command": "npx",
"args": ["-y", "agrisignal-mcp"]
}
}
}Any other MCP client that supports stdio servers can run this the same way: npx -y agrisignal-mcp.
"Should I irrigate my field near Ames, Iowa this week?" → geocode_location then get_irrigation_advice
"What's the soil like at this location — good for growing tomatoes?" → get_soil_profile
"Is there a frost risk in the next 10 days?" → get_frost_and_heat_risk
"How many growing degree days have accumulated since I planted on May 15?" → get_growing_degree_days
"Has this region been in a dry spell?" → get_dry_spell_status
Irrigation advice is a simplified single-layer soil water balance (in the spirit of FAO-56 accounting): total available water = (field capacity − wilting point) × root zone depth, projected forward using forecast precipitation minus reference evapotranspiration (ET0, not crop-specific actual ET). It ignores runoff and drainage below the root zone. Field capacity and wilting point come from SoilGrids at 15–30cm depth; current soil moisture from Open-Meteo's 9–27cm band — a reasonable but approximate match to a generic root zone, not a specific crop's.
Growing degree days use the modified method common in US extension-service guidance for corn:
Tmin is floored at the base temperature before averaging, and an optional Tmax cap can be applied.
Set floor_tmin_at_base: false for the plain average method if that fits your crop better.
Dry spell severity thresholds (mild >7 days, moderate >14, severe >21) are a general rule of thumb, not a calibrated meteorological drought index (e.g. SPI).
SoilGrids no-data pixels: geocoded place coordinates often land on a town/city center, which can be a no-data pixel in SoilGrids' 250m grid (buildings, pavement). Soil-data tools automatically retry ~1km away and say so in the response when this happens — for a real field, pass its actual coordinates rather than a town center for a more accurate reading.
Open-Meteo and SoilGrids are free services. Be considerate — avoid hammering either API with rapid repeated calls; SoilGrids in particular can take several seconds per request.
npm install
npm run build
npm testMIT
Resolve a free-text place name to ranked latitude/longitude matches. Required first step before using the other tools, which take coordinates rather than place names.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | A bare place name, e.g. 'Ames' or 'Nakuru' — not a compound 'City, Region' string, which the underlying geocoder often fails to match. Use country_code to disambiguate instead. | |
| count | No | Max number of matches to return (default 5). | |
| country_code | No | ISO 3166-1 alpha-2 country code to disambiguate, e.g. 'US'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must cover behavior. It states it returns ranked matches, implies multiple results, and warns against compound strings. However, it does not detail error handling, empty results, or response format beyond coordinates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: one for purpose and one for usage context. No unnecessary words, perfectly front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and rich schema, the description is mostly complete. It explains the purpose, relationship to siblings, and input best practices. Lacks explicit mention of output format (e.g., coordinates structure) and error scenarios, but context signals indicate no output schema, so the burden is partly on the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions for all three parameters. The tool description adds no additional parameter information beyond what the schema already provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves a free-text place name to ranked latitude/longitude matches, using a specific verb and resource. It explicitly distinguishes from sibling tools by noting it is a required first step before using them, which take coordinates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: use this tool first to obtain coordinates, then use sibling tools. It implies when not to use (when coordinates already available) and sets context effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check how many consecutive dry days a location has had and total precipitation over a lookback window, as a rough drought-risk indicator. Severity thresholds are a general rule of thumb, not a calibrated meteorological drought index.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. | |
| lookback_days | No | How many past days to examine (default 14). | |
| dry_day_threshold_mm | No | Precipitation below which a day counts as dry, in mm (default 1.0). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It does mention that severity thresholds are a general rule of thumb and not a calibrated index, which is helpful. However, it lacks details on data sources, update frequency, geographic coverage, rate limits, or what exactly the return value looks like. Significant behavioral traits are unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences. It is front-loaded, stating the core purpose first, followed by an important caveat. There is no unnecessary information, and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 4 parameters and no output schema, the description provides the essential purpose and a caveat but lacks details on what the tool returns (e.g., numerical values, severity categories) or any edge cases. It is minimally complete but could be enhanced to better inform the agent about the response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all 4 parameters, so the agent can understand their meaning from the schema. The description does not add further explanation of parameters beyond mentioning 'lookback window' and 'dry days', but this is redundant given the schema's clarity. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks consecutive dry days and total precipitation over a lookback window as a rough drought-risk indicator. It is specific about the resource (dry spell status) and the action (check). However, it does not explicitly differentiate from sibling tools like get_frost_and_heat_risk or get_growing_conditions, though the unique purpose is evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives. It implies usage for drought risk assessment, but there is no explicit statement of when to use or when not to use, nor mentions of prerequisites or contextual cues. The agent must infer usage from the tool name and purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan the daily forecast for temperature extremes that threaten crops: frost/freeze risk (low temperatures) and heat stress risk (high temperatures). Flags each day with any risk found.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of forecast days to scan (default 7). | |
| frost_c | No | Temperature at or below which frost risk is flagged, in Celsius (default 0). | |
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. | |
| hard_freeze_c | No | Temperature at or below which hard-freeze risk is flagged, in Celsius (default -2). | |
| heat_stress_c | No | Temperature at or above which heat stress risk is flagged, in Celsius (default 32). | |
| severe_heat_c | No | Temperature at or above which severe heat risk is flagged, in Celsius (default 38). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions scanning the forecast and flagging risky days, but lacks details on output format, behavior with missing parameters, or any side effects. It is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The first sentence states the purpose, the second explains the output. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should provide more context on results and defaults. It mentions flagging days but not how the risk is represented. The tool has 7 parameters but the description lacks completeness regarding their use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema itself documents parameters well. The description adds minimal parameter meaning (e.g., 'flags each day with any risk found'). Baseline is 3, and the description does not significantly enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans forecast for frost/freeze and heat stress risks, specifying the threat to crops. It distinguishes from sibling tools like get_dry_spell_status or get_growing_conditions by focusing on temperature extremes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. It simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Snapshot of current weather, recent soil temperature/moisture readings, and a short daily forecast for a location — a general-purpose 'what's happening at this field right now' summary.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. | |
| forecast_days | No | Days of daily forecast to include (default 3). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It honestly lists the data included but does not address data recency, error handling for invalid coordinates, or any rate limits. It provides a basic but incomplete picture.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that immediately conveys the tool's purpose and scope. Every word adds value, and it is front-loaded with the key verb 'Snapshot.'
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main data categories (weather, soil, forecast) but does not specify output structure or units. For a tool with no output schema, a bit more detail would enhance completeness, but it is adequate for a summary tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema descriptions are complete for all parameters (latitude, longitude, forecast_days). The tool description adds no extra detail beyond 'for a location,' so it does not improve on the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a snapshot of current weather, recent soil temperature/moisture, and a short daily forecast, defining it as a general-purpose summary for a location. This distinguishes it from sibling tools like get_frost_and_heat_risk or get_growing_degree_days which are more specialized.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description calls it a 'general-purpose...summary,' implying it should be used for an overview rather than specific data. However, it does not explicitly state when not to use it or mention alternatives by name, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate accumulated growing degree days (GDD) over a date range using the modified method (floors Tmin at the base temperature, optionally caps Tmax). GDD tracks crop heat accumulation and is commonly used to predict development stage, maturity, and pest/disease emergence windows. Supports past dates (via historical reanalysis) and future dates (via forecast, up to 16 days out).
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | Yes | End date, YYYY-MM-DD (inclusive). | |
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. | |
| start_date | Yes | Start date, YYYY-MM-DD. | |
| base_temp_c | No | Base temperature in Celsius below which no development occurs. Defaults to 10 (common for corn/soybean). | |
| upper_cap_c | No | Optional upper cap on Tmax before averaging, e.g. 30 for corn. Uncapped if omitted. | |
| floor_tmin_at_base | No | Floor Tmin at base_temp_c before averaging (the standard modified method). Defaults to true. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the modified calculation method (floors Tmin, optionally caps Tmax) and data sources (historical reanalysis, forecast up to 16 days), which is useful behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the core purpose. Every sentence adds value, with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description explains purpose and method, it lacks details on the output format (e.g., single accumulated value or time series) and possible errors. Given no output schema, this gap reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds meaning by explaining defaults (base_temp_c=10 for corn/soybean), optionality (upper_cap_c), and the standard modified method (floor_tmin_at_base), enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Calculate') and resource ('growing degree days'), clearly distinguishing it from sibling tools that focus on other agricultural indices like frost risk or irrigation advice.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the agricultural context (crop heat accumulation, development stages) and date support (historical and forecast), but does not explicitly state when not to use this tool or mention alternatives among the listed siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate whether irrigation is needed by combining soil water-holding capacity (SoilGrids), current soil moisture, and the forecast balance of precipitation vs. reference evapotranspiration (Open-Meteo). Returns a data-grounded estimate with all inputs shown, not a certified recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. | |
| forecast_days | No | Forecast window to project forward, in days (default 5). | |
| root_zone_depth_mm | No | Effective root zone depth in mm. Defaults to 400mm (typical for many row crops); use less for shallow-rooted vegetables, more for established trees/vines. | |
| management_allowed_depletion | No | Fraction of available water allowed to deplete before irrigating. Defaults to 0.5, a common general-purpose threshold. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It states that the tool combines specific data sources and returns an estimate with all inputs shown, but it does not mention whether it modifies any state, requires authentication, has rate limits, or handles errors. The disclosure that it is 'not a certified recommendation' adds some transparency, but more detail is needed for a safe and informed call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just two sentences. It front-loads the primary action ('Estimate whether irrigation is needed') and immediately specifies the data sources and limitations. Every phrase adds value, and there is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema, the description should explain what the tool returns. It states it returns 'a data-grounded estimate with all inputs shown', which is somewhat vague but gives a reasonable expectation. For a tool with 5 parameters (2 required), it provides enough context for a basic understanding, though details on the exact output structure (e.g., JSON format, fields) are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter in the input schema already has a clear description. The tool description does not add any additional meaning beyond what the schema provides. For example, 'forecast_days' and 'management_allowed_depletion' are well-documented in the schema. The baseline score of 3 is appropriate since the schema itself is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: estimating irrigation need by combining soil and weather data. It specifies data sources (SoilGrids, Open-Meteo) and distinguishes itself as a 'data-grounded estimate' rather than a certified recommendation. However, it does not explicitly differentiate from sibling tools like 'get_dry_spell_status' or 'get_soil_profile', which could be related but serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that the output is not a certified recommendation, providing a limited usage caveat. However, it does not specify when to use this tool versus alternatives like 'get_dry_spell_status' or 'get_growing_conditions', nor does it describe prerequisites (e.g., requiring soil profile data) or conditions where the tool is not appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch soil texture, pH, organic carbon, and water-holding properties for a coordinate from SoilGrids (ISRIC's global 250m-resolution soil map), at multiple depths.
| Name | Required | Description | Default |
|---|---|---|---|
| depths | No | Depth bands to query. Defaults to 0-5cm, 5-15cm, 15-30cm, 30-60cm. | |
| latitude | Yes | Latitude in decimal degrees. | |
| longitude | Yes | Longitude in decimal degrees. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses data source and resolution, but with no annotations, fails to mention response format, rate limits, auth requirements, or behavior for invalid coordinates. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence packed with essential information: action, data fields, source, resolution, depth variability. No filler, front-loaded with verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without output schema or annotations, provides sufficient context on returned data (specific soil properties) and source. Missing response structure and error handling, but adequate for a straightforward lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for latitude, longitude, and depths. Description adds 'multiple depths' and source context but no extra meaning beyond schema. Baseline score applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool fetches soil properties (texture, pH, organic carbon, water-holding) from SoilGrids for a given coordinate at multiple depths. Distinct from sibling tools focused on weather and crop advice.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies use for soil data queries, but no explicit guidance on when to choose this over siblings like get_growing_conditions. Lacks when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.1geocode_locationget_dry_spell_statusget_frost_and_heat_riskget_growing_conditionsget_growing_degree_daysget_irrigation_adviceget_soil_profileScored across 7 tools
Each tool targets a distinct agricultural or weather-related function: geocoding, dry spell, frost/heat risk, general conditions, GDD, irrigation advice, and soil profile. There is no overlap in purpose; the descriptions clearly differentiate them.
All tool names follow a consistent verb_noun pattern in snake_case, primarily using 'get_' or 'geocode_' as verbs. This makes the tool surface predictable and easy to navigate.
With 7 tools, the set is well-scoped for an agricultural signal MCP server. Each tool addresses a key aspect of agronomic decision-making without being overly numerous or sparse.
The tool set covers the core workflow: location resolution, drought, frost/heat, general conditions, GDD, irrigation, and soil data. A minor gap is the lack of a tool for direct crop stage prediction, but GDD provides a foundation for that.
Weather, climate, terrain, fire and flood risk data for any point or polygon on Earth. Free tier.
Real-time weather conditions and multi-day forecasts via Open-Meteo — free, no API key required
Access UK flood warnings, river levels, water quality, Met Office forecasts, and carbon data
Weather data, forecast API, climate data, historical weather, alerts, agricultural & travel weather.
