HeFeng Weather MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as fetching weather forecasts for China, leaving no ambiguity for an agent to misselect.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'get-weather' follows a clear verb-noun pattern, and there are no other tools to deviate from this pattern.
Tool Count2/5A single tool is too few for a weather server's apparent scope, which typically involves multiple operations like current conditions, forecasts, alerts, or location-based queries. This minimal set limits functionality and feels thin for the domain.
Completeness2/5The tool surface is severely incomplete for a weather domain. It only provides forecasts for China, missing essential operations such as current weather, historical data, alerts, or support for other regions, which will likely cause agent failures in broader tasks.
Average 3.2/5 across 1 of 1 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
- CI status not available
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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?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions geographic restriction (China) but doesn't cover other important traits: whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the output format looks like. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple weather tool and front-loads the essential information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 2 parameters. It doesn't explain what weather data is returned (e.g., temperature, precipitation), how results are structured, or any behavioral constraints. For a weather forecasting tool, users need to know what to expect beyond just 'forecast'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. It doesn't explain parameter relationships, provide examples beyond the schema's enum descriptions, or clarify edge cases. Baseline 3 is appropriate when the schema does all the work.
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?
The description clearly states the tool's purpose: '获取中国国内的天气预报' (Get weather forecast for China). It specifies both the action (get weather forecast) and the geographic scope (China), though it doesn't distinguish from siblings since none exist. The description is specific but could be more precise about what type of weather data is returned.
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 usage context (weather forecasting for China) but provides no explicit guidance on when to use this tool versus alternatives. With no sibling tools, there's no need to differentiate, but it doesn't mention prerequisites, limitations, or ideal use cases beyond the basic scope.
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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