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komaksym

chatgpt-quota-mcp

by komaksym
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of overlap or confusion. The tool's purpose is unambiguous and clearly distinct.

    Naming Consistency5/5

    The single tool name 'get_chatgpt_quota' follows a clear verb_noun pattern, consistent with common MCP server conventions. Since there's only one tool, consistency is trivially maintained.

    Tool Count4/5

    The server has a focused scope on ChatGPT quota; one tool is minimal but appropriate for a read-only quota endpoint. It sits on the low end of typical counts but is not excessive for the stated purpose.

    Completeness5/5

    The tool provides a complete picture of quota status including windows, usage, and reset times. For a quota-information server, there are no obvious missing operations.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 7 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

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  • 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, the description carries the full burden. It indicates a read-only behavior via 'Return' and describes a point-in-time snapshot with 'current'. It does not mention side effects or quota consumption, but the nature of the tool is clearly non-mutating and transparent for an agent to infer.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence that is front-loaded with the verb 'Return' and lists the key data items. Every word adds value, with no fluff or redundancy.

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

    Completeness5/5

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

    For a zero-parameter tool with an output schema, the description fully captures the tool's purpose and expected data. The itemized list of returned data (quota windows, remaining usage, reset times) is sufficient for an agent, and the output schema provides detailed structure.

    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 description trivially covers all schema semantics. The baseline for 0-parameter tools is 4, and the description adds no extra parameter information because none exists.

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

    Purpose5/5

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

    The description uses the specific verb 'Return' and clearly identifies the resource (ChatGPT/Codex quota) and the data provided (quota windows, remaining usage, reset times). It is unambiguous and fully distinguishes the tool's function, even without siblings.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies the tool should be used to check current quota status before making calls. It clearly states what data is returned, providing context for when to use it. No exclusions or alternatives are needed since there are no sibling tools, but explicit when-to-use guidance is minimal.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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