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hkopenai

Hong Kong Government Environment MCP Server

by hkopenai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.8

  • Disambiguation5/5

    With only a single tool, there is no possibility of confusion between tools. The tool's purpose is clearly scoped to retrieving the current AQHI.

    Naming Consistency5/5

    The lone tool name follows a clear verb_noun pattern: get_current_aqhi. There is no other tool to create inconsistency.

    Tool Count2/5

    The server presents itself as a general environment MCP for Hong Kong, yet it exposes only one tool for air quality. This is too few for the apparent scope, making the server feel extremely thin.

    Completeness1/5

    Only current AQHI readings are provided, with no historical data, forecasts, or coverage of other environmental domains. The surface is severely incomplete for an environment-focused server.

  • Average 3.8/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • 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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the behavioral burden. It usefully discloses the data granularity (individual stations), the measurement scale (1 to 10 and 10+), and the health risk categories with advice. It does not mention data freshness or update frequency, but for a simple read-only current-data tool this is adequate.

    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 compact two-sentence definition that front-loads the key fact: this is current AQHI data. It could be slightly more direct by using an imperative verb, but there is no fluff or repetition.

    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 no-parameter read-only tool with an output schema, the description covers the essential context: what is measured, where, the scale, and the categorization. Nothing critical is missing for an agent to select and invoke the tool correctly.

    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 tool has zero parameters, so the baseline is 4. No parameter guidance is needed, and the description appropriately focuses on the output semantics instead.

    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 clearly identifies the resource: current AQHI readings at individual general and roadside monitoring stations in Hong Kong. It lacks an explicit verb like 'retrieves' or 'returns,' but the meaning is unambiguous. With no sibling tools, differentiation is not needed.

    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 implies this is the tool for obtaining current AQHI values in Hong Kong, but it does not explicitly state when to use it versus alternatives or mention any exclusions. There are no sibling tools to contrast with, so this is a minor gap.

    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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