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BACH-AI-Tools

Timezone By Location MCP Server

by BACH-AI-Tools
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Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation1/5

    The sole tool has an unclear purpose; its description is a placeholder asking to manually add endpoint info, providing no functional clarity. With ambiguous intent, agents cannot reliably select it.

    Naming Consistency1/5

    The tool name 'api_endpoint' is a noun, not a verb-based action, and its description suggests configuration rather than a callable operation. No consistent pattern exists with only a single vague name.

    Tool Count1/5

    A single tool is insufficient for a timezone-by-location service, and it is not even a functional tool but a placeholder, making the count inappropriate.

    Completeness1/5

    The server offers no actual timezone lookup functionality; the only tool is a stub, so the domain is entirely uncovered.

  • Average 1.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior1/5

    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 mention any side effects, permissions, persistence, reversibility, or return behavior. For a tool that 'adds' information, there is no indication of what changes occur or what the output looks like.

    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 concise (one sentence) and front-loaded, but it is under-specified. It earns its place only slightly by mentioning RapidAPI as a source, yet the brevity does not compensate for the lack of meaningful content.

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

    Completeness1/5

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

    For a tool with one optional parameter and no output schema, the description is extremely incomplete. It fails to explain the tool's purpose, how the parameter relates to the endpoint information, what the expected behavior is, or what the user should observe after invocation.

    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 schema has one optional parameter 'param' with a description '参数' (parameter), which is tautological and provides no real semantics. The tool description adds nothing about how to use the parameter or what values it expects. Although schema coverage is 100%, the content is useless, and the description does not compensate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Please manually add endpoint information based on the RapidAPI page' uses a specific verb ('add') but the resource ('endpoint information') is vague and the phrase reads more like an instruction to a human than a functional tool description. It does not clearly define what the tool actually does or what constitutes endpoint information, making it barely rise above a tautology.

    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 versus alternatives. The phrase 'based on the RapidAPI page' implies a context but does not explicitly state usage scenarios, prerequisites, or exclusions. No alternatives are mentioned.

    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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