A
licenseNot graded
qualityC
maintenanceEnables querying historical and future climate projections (1950–2050) for temperature and precipitation, and comparing climate models to assess agreement and uncertainty.
13
MIT
NOAA Climate Data
by com.olyport
Server Details
Historical climate data, temperatures, precipitation, and normals
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- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.4/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a distinct role: station discovery, historical data retrieval, and normals retrieval. The descriptions clearly differentiate the two data retrieval tools by their outputs and parameters. No overlap would cause misselection.
Naming Consistency5/5
All three tools follow a consistent verb_noun pattern with snake_case. The verbs find and get are both appropriate for retrieval actions and follow a clear convention.
Tool Count5/5
With only 3 tools, the server is tightly focused on its purpose. Each tool is necessary and the count is within the natural range for a small domain-specific server.
Completeness4/5
The core workflow of finding stations and retrieving climate data or normals is covered. Minor gaps include no tool for listing available datasets or data types, but the provided examples make the surface workable.
Available Tools
3 toolsfind_stationsAInspect
Find NOAA weather stations in an area.
Returns a list of weather stations with their IDs, names, coordinates,
and active date ranges. Use the station IDs with get_climate_data.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'NY').
county_fips: Five-digit county FIPS code (e.g. '36061' for Manhattan).
dataset: Dataset ID to filter stations that have data in this dataset.
Default is 'GHCND' (daily summaries).
limit: Maximum number of stations to return (default 25, max 1000).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| dataset | No | GHCND | |
| county_fips | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses the return format (IDs, names, coordinates, active date ranges), the default dataset ('GHCND'), and the maximum limit (1000). It does not specify whether state or county_fips is required or how these filters interact, but for a read-only lookup tool, the coverage is solid.
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 front-loaded with the purpose, then provides return behavior and a concise, well-formatted Args section. Every sentence adds value and is structured for quick scanning.
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?
Given four parameters, zero schema descriptions, and no annotations, the description covers all parameters and return behavior. The only notable gap is the lack of clarity on whether state and county_fips are required, optional, or mutually exclusive. For an otherwise thorough description, this is a minor but real omission.
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 provides no property descriptions, so the description fully compensates. It explains each parameter with examples (e.g., 'CA', 'NY'; '36061' for Manhattan), specifies the dataset default, and clarifies the limit's maximum. This is exactly the level of detail needed for correct invocation.
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 a specific verb and resource: 'Find NOAA weather stations in an area.' It clearly distinguishes this tool from siblings like get_climate_data and get_climate_normals by focusing on station discovery. The return fields are also summarized, reinforcing the tool's purpose.
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 explicitly states the downstream usage: 'Use the station IDs with get_climate_data.' This provides clear context for when to use the tool. However, it does not mention when not to use it or explicitly contrast it with get_climate_normals, so it falls just short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_climate_dataAInspect
Get climate observations from NOAA Climate Data Online.
Returns historical weather measurements such as temperature, precipitation,
and snowfall. You must provide either a station_id or a FIPS code to
identify the location, and a date range.
Args:
station_id: NOAA station identifier (e.g. 'GHCND:USW00094728' for Central Park).
fips: FIPS code for county or state (e.g. '36' for New York state, '36061' for Manhattan).
dataset: Dataset ID. Common values: 'GHCND' (daily summaries), 'GSOM' (monthly),
'GSOY' (annual), 'NORMAL_DLY' (daily normals). Default is 'GHCND'.
start_date: Start date in YYYY-MM-DD format. Required for most datasets.
end_date: End date in YYYY-MM-DD format. Required for most datasets.
data_types: Comma-separated data type IDs to filter. Common types:
TMAX (max temp), TMIN (min temp), TAVG (avg temp),
PRCP (precipitation), SNOW (snowfall), SNWD (snow depth),
AWND (avg wind speed). If omitted, all available types are returned.
limit: Maximum number of records to return (default 100, max 1000).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| fips | No | ||
| limit | No | ||
| dataset | No | GHCND | |
| end_date | No | ||
| data_types | No | ||
| start_date | No | ||
| station_id | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses key behaviors: returns historical weather measurements, default limit of 100 and max 1000, and that omitting data_types returns all available types. It does not discuss error handling or rate limits, but the existence of an output schema reduces the need to explain return structure.
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 well-structured with a short overview followed by an Args list. It is appropriately sized for seven parameters and avoids unnecessary fluff. Minor repetition exists (e.g., defaults are stated both in prose and in the Args list), but this does not harm clarity.
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 seven-parameter tool with zero schema descriptions and no annotations, the description covers all parameters, provides examples, and clarifies optional defaults. The output schema exists, so the lack of return-value explanation is acceptable. It could be slightly more complete by noting dataset-specific prerequisites or explicit sibling tool guidance, but overall it is highly usable.
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 input schema provides no descriptions (0% schema description coverage), yet the description fully compensates by explaining every parameter with concrete examples: station_id ('GHCND:USW00094728' for Central Park), FIPS ('36' for New York, '36061' for Manhattan), dataset values, date format, data_types with common IDs, and limit defaults. This gives the agent far more than the schema alone.
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 action ('Get climate observations from NOAA Climate Data Online') and the resource ('NOAA Climate Data Online'), with concrete examples of returned data ('temperature, precipitation, and snowfall'). This distinguishes it from sibling tools like find_stations (station lookup) and get_climate_normals (normals, not historical observations).
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 states the mandatory inputs ('You must provide either a station_id or a FIPS code ... and a date range'), which is a key usage requirement. However, it does not explicitly mention when to use this tool instead of the sibling tools, nor does it reference alternatives for station lookup (find_stations) or normals (get_climate_normals).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_climate_normalsAInspect
Get 30-year climate normal values for a NOAA weather station.
Climate normals are averages computed over the most recent 30-year period
(currently 1991-2020). They represent typical conditions for a location
and are useful for comparing current conditions to historical baselines.
Args:
station_id: NOAA station identifier (e.g. 'GHCND:USW00094728').
Use find_stations to look up station IDs.
data_types: Comma-separated normal data type IDs to filter. Common types:
DLY-TMAX-NORMAL (avg daily max temp), DLY-TMIN-NORMAL (avg daily min temp),
DLY-TAVG-NORMAL (avg daily temp), DLY-PRCP-PCTALL-GE001HI (precip probability),
MTD-PRCP-NORMAL (monthly precip), ANN-TMAX-NORMAL (annual max temp).
If omitted, all available normals are returned.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data_types | No | ||
| station_id | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
A4.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses that normals are based on the 1991-2020 period and notes default behavior when data_types is omitted. However, it does not mention potential limitations, units, or response characteristics, leaving some behavioral aspects undisclosed.
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 concise and well-structured, with a brief overview followed by an Args section. Every sentence adds value, including the definition, usage context, and parameter guidance, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two parameters and an output schema present, the description provides sufficient context: what the tool returns, how to specify parameters, and how to find station IDs. No further details are needed for effective invocation.
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 input schema has no property descriptions (0% coverage), so the description fully compensates. It specifies station_id format with an example, explains data_types as comma-separated IDs, lists common type examples, and states the default behavior when omitted.
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 'Get 30-year climate normal values for a NOAA weather station,' using a specific verb and resource. It differentiates from siblings by emphasizing 'normal values' and the 30-year period, which is distinct from find_stations and get_climate_data.
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 explains when climate normals are useful ('useful for comparing current conditions to historical baselines') and directs users to use find_stations to look up station IDs. It lacks explicit exclusion of get_climate_data, but the context makes the appropriate use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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