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NG-Bullseye

claude-usage-mcp

by NG-Bullseye
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TypeScript
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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

  • Disambiguation4/5

    The two tools are mostly distinct: get_usage returns comprehensive usage data for all windows, while get_velocity returns only the velocity recommendation for a single window. However, get_usage already includes velocity recommendations, so an agent might be unclear whether to use get_velocity or extract from get_usage.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_usage, get_velocity), making them predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the server is on the low side but still reasonable for a focused purpose. The tools cover a narrow domain well without being too sparse.

    Completeness4/5

    The server covers the core use case of monitoring Claude usage and velocity recommendations. There are no obvious missing operations for its stated purpose, though it could optionally include a tool for a specific window's full data without velocity.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 fully carries the burden. It discloses authentication method (OAuth session reuse) and the data returned (utilization, forecast, etc.). It is a read-only retrieval, though not explicitly stated. The description adds valuable behavioral context beyond what schema alone provides.

    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 sentence that packs a lot of information without being overly verbose. It is well-structured and front-loaded with the core function. Slightly long but still concise given the detail.

    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 parameterless tool with no output schema, the description provides comprehensive details about the returned data (utilization, forecast, limit, velocity) and authentication. It lacks mention of error conditions but is otherwise complete for typical usage.

    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?

    There are no parameters, so the schema coverage is 100%. The description adds no parameter info because none exist. A baseline of 4 is appropriate for zero parameters.

    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 specifies exactly what the tool does: get current Claude subscription usage for every window, including utilization, reset time, forecast, limit hit time, and velocity recommendation. It clearly distinguishes from the sibling tool 'get_velocity' by detailing the broader scope beyond just velocity.

    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 states it reuses Claude Code's OAuth session and that no API key is needed, but does not explicitly say when to use this tool vs. alternatives like 'get_velocity'. The context is implied but not explicitly contrasted.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. It explains the return value range (0-120%) and interpretation for different values, as well as window options. However, it does not mention if the operation is read-only or any permissions needed.

    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 concise single paragraph with three front-loaded sentences. Each sentence adds essential information without wasted words.

    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?

    Given one parameter with full schema coverage and no output schema, the description covers the core functionality and return interpretation. It lacks details on error handling or default behavior but is largely complete for the tool's simplicity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds significant meaning to the single parameter 'window' by explaining each enum value ('5h' as rolling 5-hour, 'weekly' as 7-day, 'weekly_opus') beyond the schema's simple description 'Which limit window to evaluate.'

    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 clearly states it returns a velocity recommendation for one window, with specific meaning for 100%, <100%, and >100%. It distinguishes from sibling 'get_usage' by focusing on recommendation rather than raw usage.

    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 the tool is for getting velocity recommendations for a given window but does not explicitly state when to use it over alternatives or provide exclusion criteria.

    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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  • Evaluate tool definition quality.

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