Weather API167 MCP Server

Server Quality Checklist

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Latest release: v2.0.2
Disambiguation5/5
All tools have distinct purposes: current weather, forecast, air pollution, earthquakes, US alerts, US zone list, and country info. No overlapping functionality that would confuse an agent.
Naming Consistency5/5
All tool names follow snake_case and a consistent pattern of descriptor_noun (e.g., current_weather, air_pollution_data, us_zone_list). No mixing of conventions.
Tool Count5/5
Seven tools is a reasonable scope for a weather API server, covering core weather data, forecasts, air pollution, earthquakes, and US-specific alerts without being overwhelming or too sparse.
Completeness3/5
The set covers basic weather, forecast, air pollution, earthquakes, and US alerts, but misses historical weather, global severe warnings, and non-US alerts. These are notable but not critical gaps for a basic weather server.
Average 3.1/5 across 7 of 7 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on November 28, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose any behavioral traits such as data freshness, rate limits, or what occurs when no data matches. Without annotations, the description provides no information about side effects or operational characteristics beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it is underspecified. It could benefit from clarifying the result type without adding significant length.
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 data retrieval tool with three parameters and no output schema, the description fails to explain what the response contains (e.g., list of events, details). The absence of information about required fields (none marked required) further reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage but parameter descriptions are minimal (e.g., 'Example value: US' for country). The tool description adds no additional context about expected formats (e.g., date format) or how parameters affect the query, leaving the agent to infer usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it provides 'near real time earthquake for given country', which indicates the domain but lacks a specific verb (e.g., retrieve, list). It is clear it is earthquake data and distinguishes from sibling weather and pollution tools, but the exact nature of the result (list vs. single event) is ambiguous.
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?
No guidance is given on when to use this tool versus alternatives like forecast_weather or current_weather. There is no mention of prerequisites, limitations, or conditions under which the tool should or should not be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the behavioral burden. It fails to disclose traits like data freshness, whether it's read-only, pagination, or what happens with no parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence, but it is vague and poorly structured ('for given region or area and other filtering parameter'). Could be more precise and front-loaded with the action.
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?
No output schema, yet the description does not explain return format or structure. With 10 optional parameters and no required ones, lacks guidance on valid combinations or defaults.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% but descriptions are minimal (e.g., examples only). The tool description adds no parameter info beyond the schema, missing meanings for codes like 'AM' or 'AL'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it provides 'US weather alert daily information' for a region/area, which identifies the resource but lacks a specific verb (e.g., 'retrieve', 'list'). It does not differentiate from siblings like current_weather or forecast_weather.
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?
No guidance on when to use this tool versus alternatives. The description only implies general usage for a given region/area, with no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'Fetch' but lacks details on data freshness, rate limits, or whether it returns current or historical data. Without output schema, agent must guess response format.
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?
Single sentence, no redundant information. Efficiently conveys the core action and input types without filler.
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?
With 5 optional parameters, no output schema, and zero annotations, the description is too sparse. Agent lacks information about required fields, data structure, or error handling. Incomplete for practical 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?
Schema coverage is 100% with descriptions explaining parameter priority. The description adds minimal value ('using coordinate value, place name or zip code') but does not improve on schema detail. Baseline 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?
Description clearly states the verb 'Fetch' and resource 'air pollution data', and specifies three input methods (coordinate, place, zip). While it doesn't explicitly differentiate from siblings like 'current_weather', the tool name and resource are distinct enough for an agent to infer purpose.
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?
No guidance on when to use this tool versus alternatives. Description does not specify scenarios (e.g., checking air quality vs. weather) or exclusions. Agent must rely solely on tool name and sibling names, which is insufficient for confident selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose any behavioral traits such as rate limits, authentication, or what 'detail' entails (e.g., population, capital).
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 sentence with no unnecessary words, achieving maximum conciseness.
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 lack of output schema and minimal parameter info, the description is too brief to fully specify the tool's functionality. It omits what 'detail' includes and any constraints.
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?
Schema description coverage is 100% with one parameter having an example. The description adds no extra meaning beyond the schema; it does not clarify valid values or the effect of omitting the optional parameter.
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 states 'Return detail for given country', which clearly indicates the verb (return) and resource (detail for a country). It is distinct from sibling tools that focus on weather and pollution data.
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?
No guidance on when to use this tool versus alternatives. It provides no context for when it is appropriate or when to avoid it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behaviors. It only says 'fetch', implying a read operation, but omits details like auth needs, rate limits, data freshness, or that coordinates have priority. Minimal transparency.
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 a single sentence of 13 words – concise and front-loaded with the core purpose. No extraneous wording, but it could be slightly more structured.
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?
With 9 parameters, no required fields, and no output schema, the description is insufficient. It doesn't explain return format, cnt limit, units meanings, or what type/mode/lang do. Users would lack essential context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining priority rules (lat/lon over place over zip) and that lat/lon are more accurate. This complements the schema descriptions, though other parameters like type, mode, lang lack additional context.
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 states the tool fetches forecast weather data using coordinates, place name, or zip code. It clearly identifies the resource (forecast weather) and how to specify location, though it doesn't explicitly differentiate from sibling tools like current_weather.
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?
No guidance is provided on when to use this tool versus alternatives like current_weather or earthquake_data. There are no scenarios or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It fails to mention data freshness, rate limits, authentication, or response format. The tool's behavior beyond fetching is opaque, limiting transparency.
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 of 12 words that directly states the tool's action and inputs. Every word contributes meaning, with no redundancy or filler.
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 has 7 parameters and no output schema, the description should explain what data is returned (e.g., temperature, humidity) and clarify behavior for missing inputs. It only mentions location methods, leaving significant gaps for an agent to understand the tool fully.
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?
Schema coverage is 100%, so baseline is 3. The description reiterates the three location methods but adds no new meaning beyond the schema's detailed parameter descriptions. There is no benefit or penalty beyond baseline.
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 fetches 'current weather data' using specific location methods (coordinate, place, zip). This distinguishes it from sibling tools like 'forecast_weather' and 'air_pollution_data', providing a clear 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 outlines three location input methods but does not explicitly state when to prefer this tool over siblings. However, the sibling names (e.g., 'forecast_weather', 'air_pollution_data') imply differentiation, and the schema elaborates on parameter priority.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the main behavior (filtered or all zones list) but is minimal. No mention of authentication, rate limits, or zone format. Adequate but could add detail.
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?
Single sentence, front-loaded with key info, no superfluous words. Efficient and clear.
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 low complexity (one optional parameter, no output schema), the description covers main usage. Could define what 'zone' means for clarity, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the schema: explains that state is optional and filters the list, while omitting returns all zones. Schema already has example value, but description clarifies the conditional logic.
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?
Description clearly states the tool provides a zone list for a US state, or all zones if no state is given. The verb 'list' and resource 'zone' are explicit, and it distinguishes from sibling tools like weather or earthquake 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 tells when to use the tool (for US zone list) and how the state parameter affects output. No explicit alternatives or when-not-to-use, but sibling tools are in different domains so no confusion.
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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