MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
Get information about who built this MCP server and why.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. It correctly implies a read-only information retrieval operation, but it doesn't explicitly state that there are no side effects, no external calls, or any other behavioral guarantees. For a simple metadata tool this is adequate but not richly transparent.
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?
A single, well-structured sentence that front-loads the verb ('Get') and resource ('information about who built this MCP server and why'). Every word earns its place, with no redundancy or filler.
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 has no parameters, an output schema is present to describe return values, and the sibling tools are clearly unrelated, the description is fully complete. An agent has everything needed to correctly invoke this tool and interpret its purpose.
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 tool has zero parameters, so the baseline of 4 applies. There are no parameter semantics to explain, and the description correctly hints at the tool's output without needing to reference any parameters.
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 specific action ('Get information') and the resource ('who built this MCP server and why'). This is immediately distinct from the weather-focused sibling tools and leaves no ambiguity about the tool's function.
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 clear context for use: whenever an agent needs to know about the server's origin or rationale. It doesn't explicitly mention alternatives, but the sibling tools are so functionally different that no exclusion is necessary for correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare current weather between two cities.
| Name | Required | Description | Default |
|---|---|---|---|
| city_a | Yes | ||
| city_b | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It usefully scopes the data to 'current' weather and implies a read-only comparison, but it does not mention failure behavior, units, or data source limitations. This is adequate for a simple query tool, with some gaps.
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?
A single sentence with no filler; the verb and target are front-loaded. Every part of the description contributes to understanding the tool.
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 tool is simple and an output schema exists, so return-value details need not be in the description. However, the definition lacks guidance on city-name formatting and does not explicitly contrast with get_weather, leaving minor but relevant gaps for an agent selecting the 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 description coverage is 0%, and the description does not compensate by explaining how city names should be formatted or disambiguated (e.g., 'London, UK' vs 'London'). The names city_a and city_b are self-evident, but no additional meaning is added.
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 ('Compare') and a clear resource ('current weather between two cities'), so an agent knows exactly what operation is offered. It does not explicitly name sibling tools, but the two-city scope distinguishes it from get_weather.
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 phrase 'between two cities' implies when the tool should be used, but the description does not explicitly say to prefer get_weather for a single city or mention any exclusions. Usage context is reasonably clear, but alternatives are left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current weather and today's forecast for a city.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of explaining behavior. It only states that current weather and today's forecast are returned, but does not disclose units, data source, freshness, error behavior, or any quirks beyond the obvious read-only nature implied by 'get'.
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, front-loaded sentence with no filler. Every word adds meaning, and its length is appropriate for a tool with one required parameter.
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 tool is simple, has one required parameter, and an output schema is present, so the basic call shape is minimally viable. However, with no annotations, no parameter format details, and no usage routing among siblings, the description is only adequate, not complete.
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 0% for the single city parameter, and the description merely says 'for a city' without adding format, examples, or caveats. The agent still does not know whether to pass a city name, coordinates, or a regional identifier.
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 names a specific action and resource: getting current weather and today's forecast for a city. It is clear and naturally distinct from siblings like compare_weather or should_i_carry_umbrella, though it does not explicitly call out that distinction.
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?
There is no guidance on when to use this tool versus its siblings. The description does not mention alternatives or conditions such as 'for comparisons use compare_weather' or 'for advice use is_good_for_outdoors', leaving the agent to infer routing from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if today's weather is good for outdoor activities like a walk or run.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral transparency burden. It implies a yes/no judgment about outdoor suitability but does not disclose what criteria define 'good' weather or explicitly state that it returns a boolean. It is not misleading, but it provides minimal behavioral detail.
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?
A single front-loaded sentence with no filler. It conveys the purpose and gives helpful examples, making it appropriately concise for a simple one-parameter predicate tool.
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?
For a low-complexity tool with an output schema, the definition is mostly sufficient: the agent understands the goal, and the required city parameter is visible in the schema. The subjective meaning of 'good' and the lack of any explicit parameter mention leave a small but noticeable gap.
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 coverage is 0%, and the description never mentions the required city parameter. The parameter name and type are fairly self-explanatory, but the description adds no meaning about what city values are expected or how the city is used.
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 states a specific verb ('check') and resource ('today's weather' for outdoor activities) with concrete examples like 'walk or run.' The purpose is clear, though it does not explicitly contrast with sibling tools such as should_i_carry_umbrella.
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 context is implied: an agent should call this when deciding whether to do an outdoor activity today. However, there is no explicit guidance on when to use this instead of get_weather or should_i_carry_umbrella, so selection is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a yes/no recommendation on carrying an umbrella today, with reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It does disclose the core output (yes/no recommendation plus reasoning), but it does not mention edge cases, data sources, timezone handling, or failure behavior for unknown cities.
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, tightly worded sentence with no filler. It front-loads the purpose and adds the 'with reasoning' detail without unnecessary elaboration.
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 tool is simple with one obvious parameter and an output schema, so the description is mostly complete for invoking it correctly. A minor gap is the absence of guidance on city name format and how 'today' is interpreted.
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 0%, and the description does not explain the 'city' parameter beyond its name. While 'city' is fairly self-explanatory, no format or example is given, so the description fails to compensate for the missing schema details.
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 ('Get') and clearly states the resource/outcome: a yes/no umbrella recommendation with reasoning. It is distinct from siblings like get_weather, though it does not explicitly differentiate itself from is_good_for_outdoors.
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 intended use is implied by the phrasing: use this when you need a recommendation about carrying an umbrella today. However, it gives no explicit guidance about when to prefer this over sibling tools such as is_good_for_outdoors or get_weather.
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. Dates show when Glama detected each change.
aboutcompare_weatherget_weatheris_good_for_outdoorsshould_i_carry_umbrellaClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
Smarter Weather MCP: forecasts, alerts, outlooks, observations, AQI, grids, and map imagery.
Add one secure layer between your agents and this server.
Each tool has a distinct output purpose: raw weather retrieval, comparison, and two different decision-support helpers. The overlapping use of weather data does not create real ambiguity because the requested output differs clearly.
Names are all readable lowercase snake_case, but they mix conventions: get_weather and compare_weather are verb_noun while is_good_for_outdoors and should_i_carry_umbrella are sentence-style predicates, and about is a bare noun. The inconsistency is not chaotic, but an agent cannot infer a single naming pattern.
Five tools is well-scoped for a weather assistant: one metadata tool, one retrieval tool, one comparison tool, and two focused advisory tools. None of the tools feel redundant or unnecessary.
The set covers current conditions, today's forecast, comparisons, and common decision queries without dead ends. It lacks multi-day forecasts, weather alerts, and location search, but these are minor gaps for a today-focused weather utility.