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hkopenai

hk-misc-mcp-server

by hkopenai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.8

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly and specifically described as retrieving Hong Kong government auction data.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (get_government_auction_data), which is consistent and descriptive. There are no other tools to introduce inconsistency.

    Tool Count2/5

    The server has only one tool, which is too few for the apparent scope implied by the name 'hk-misc-mcp-server'. A miscellaneous Hong Kong data server would be expected to offer multiple tools covering various datasets, making the current single-tool count inadequate.

    Completeness2/5

    The server covers only auction data, leaving many other likely Hong Kong government datasets unaddressed. Even within auction data, there are likely gaps such as filtering, searching, or viewing individual auction details. The overall surface is severely incomplete for the implied miscellaneous data server purpose.

  • Average 2.9/5 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

  • 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 of behavioral disclosure. It does not specify whether the operation is read-only, how results are returned, pagination, or any potential system interactions. The only implied behavior is the 'get' action, but no details are given.

    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, short sentence that is easy to scan and front-loaded with the primary data source. However, it is a sentence fragment lacking a verb, which slightly reduces structural quality. It remains concise and free of filler.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the fully described schema and the presence of an output schema, the description adequately identifies the data source and categories. It does not mention the date-range filtering or language options, but these are covered in the schema. Overall, it is functional but not particularly rich in context.

    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?

    The schema provides full descriptions for all five parameters, so the description does not need to elaborate on them. The description adds no extra semantic context beyond the schema, warranting the baseline score of 3.

    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 specifies the exact data source (Government Logistics Department Hong Kong) and types (confiscated, used/surplus, unclaimed stores), making the tool's focus clear. However, it is a noun phrase rather than a verb phrase, so it relies on the tool name to convey the action of 'get'. It does not explicitly state 'retrieves' or 'lists'.

    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?

    There is no guidance on when to use this tool, no mention of alternatives, prerequisites, or scheduling. The description only states what data is included, leaving the agent to infer usage context from the name and schema.

    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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  • Confirm that there are no obvious security issues.
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