roc-cwa-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool serves a distinct purpose: one for 1-week forecast, one for 3-day forecast, and one for historical rainfall. No overlap or ambiguity.
Naming Consistency5/5All tools follow a consistent verb_get + time_period + resource pattern in snake_case, making it predictable.
Tool Count5/5Three tools cover the essential weather data needs without being excessive or insufficient.
Completeness4/5Covers forecasts and historical rainfall well, but missing current conditions or severe weather alerts. Minor gap.
Average 3.5/5 across 3 of 3 tools scored.
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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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?
With no annotations, the description should disclose behavior. It mentions returning cleaned data but omits details like read-only nature, error handling, or data freshness. The description is insufficient for full behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no redundant information. The structure is front-loaded with the action. Could benefit from a brief note on output structure, but remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description provides the essential purpose and a high-level return summary. However, it lacks specifics like station identifiers, date range constraints, or any caveats about data availability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no parameters, so baseline is 4. Description adds minimal value but confirms the simple invocation without inputs, which is sufficient for parameterless tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it retrieves rainfall data for the past three days, distinguishing from sibling tools that provide general weather forecasts or longer periods.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus siblings or alternatives. Usage is implied by the specific focus on historical rainfall, but lacks when-not or exclusion criteria.
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 full burden. It only states basic functionality without disclosing data source, update frequency, accuracy, or any side effects. The absence of any behavioral context beyond 'get' is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with Args and Returns sections, is mostly concise, and front-loaded. The list of valid names is necessary for usability but slightly lengthy; still efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the parameter and return type, but the return structure is vague ('various weather elements and their time series'). Given no output schema, more detail on the return format would improve completeness. Adequate for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the tool description exhaustively lists all valid Taiwan county/city names for the 'location_name' parameter and explains its meaning. This fully compensates for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets a 1-week weather forecast for a specified location. It distinguishes from sibling tools (get_3_days_weather and get_historical_rainfall) by specifying the time range and data type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for obtaining a 1-week forecast but does not provide explicit guidance on when to use it vs. alternatives or any prerequisites. Usage is inferred from the tool name and sibling context.
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 are provided, so the description must carry the behavioral burden. It states that the tool returns 'cleaned weather data containing various weather elements and their time series', but does not disclose any additional behavioral traits such as authentication, rate limits, or side effects. The verb 'get' implies a read operation, but more detail would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a clear first sentence stating the purpose, followed by an Args section and Returns section. It is mostly concise, though the list of valid county/city names adds length but provides necessary detail. Front-loading is good.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, no annotations, no output schema, and the presence of sibling tools (get_1_week_weather, get_historical_rainfall), the description adequately covers the parameter and basic purpose. However, it does not explain the return format in detail or provide guidance on when to use this versus siblings, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description adds significant meaning to the single parameter. It explicitly states 'must be a valid Taiwan county/city name' and provides a complete list of valid values, which is beyond what the schema provides (just a string type).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get 3-day weather forecast data for the specified county/city', providing a specific verb and resource. The scope is further refined by listing valid Taiwan county/city names. The tool name and description distinguish it from siblings like 'get_1_week_weather' by specifying the 3-day forecast period.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes the required input (location_name) and lists valid values, but does not explicitly state when to use this tool over siblings or provide any context on alternatives. Usage is implied but not explicitly guided.
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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