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mvacaporale

Claude Usage MCP Server

by mvacaporale
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: authentication checking, authentication initiation, and usage data retrieval. The descriptions make it easy to differentiate when to use each tool, avoiding misselection.

    Naming Consistency5/5

    All tools follow a consistent snake_case pattern with clear verb_noun structure (check_claude_auth, claude_login, get_claude_usage). The naming is predictable and readable throughout the set.

    Tool Count3/5

    Three tools is borderline thin for a usage monitoring server, as it covers only authentication and data fetching. While functional, additional tools for configuration, historical data, or notifications would make the scope more complete.

    Completeness4/5

    The tools cover the core workflow of authentication and usage retrieval well, with no dead ends. A minor gap exists in lacking tools for managing authentication sessions (e.g., logout) or advanced usage analytics, but agents can work around this.

  • Average 3.8/5 across 3 of 3 tools scored. Lowest: 3.2/5.

    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 status not available

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to specify what constitutes a successful check (boolean return? user object? exception?), whether the check modifies session state, or what latency/network requirements exist.

    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?

    Single sentence of ten words with zero redundancy. The core purpose is front-loaded and immediately comprehensible. Every word earns its place.

    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?

    Adequate for a zero-parameter tool but lacks critical information about return values since no output schema exists. For a status-checking tool, the description should ideally indicate what 'authenticated' means in terms of response data or side effects.

    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?

    Zero parameters with 100% schema coverage means the description carries baseline expectations. No parameter explanation is required, though the description implicitly confirms no inputs are needed to perform this check.

    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?

    States a specific action ('Check') and clear scope ('current session is authenticated with Claude'). However, it does not explicitly differentiate from sibling tool 'claude_login' (which establishes auth) despite the distinct purposes being inferable from names alone.

    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?

    Provides no guidance on when to invoke this tool versus alternatives. Does not indicate whether it should be called before other Claude operations, what to do if authentication fails, or how it relates to the 'claude_login' workflow.

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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. It successfully discloses that the tool returns 'daily token usage information,' which is crucial given the lack of output schema. However, it fails to mention authentication requirements, rate limiting, supported date ranges, or whether the data is cached versus live.

    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?

    Two efficient sentences: first establishes the action and target, second describes the return value. No redundancy or filler content. Information is front-loaded with the verb 'Fetch.'

    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?

    While the description explains return values (necessary without output schema), it omits critical context given the sibling tools: authentication prerequisites and token/date range limitations. For a dashboard-accessing tool with auth siblings, mentioning the required auth state would make this complete.

    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?

    Zero parameters present, establishing a baseline of 4 per scoring rules. The description does not need to compensate for missing parameter documentation since the input schema requires none.

    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?

    Uses specific verb 'Fetch' with clear resource 'Claude usage data' and source 'dashboard.' The mention of 'daily token usage' precisely scopes the returned data. Clearly distinguishes from auth-related siblings (check_claude_auth, claude_login) by focusing on data retrieval rather than authentication.

    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?

    Provides no explicit guidance on when to use versus alternatives, prerequisites, or sequencing. Given the authentication-related siblings, the description should state that authentication is required first or recommend using check_claude_auth beforehand, but it remains silent on workflow.

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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. It adds critical behavioral context that this opens a browser window (interactive UI) rather than returning a URL or token silently. However, it omits completion behavior (blocking vs async), success indicators, or failure handling.

    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?

    Two well-structured sentences: first states purpose, second states usage condition. Front-loaded with action, zero redundancy, appropriate length for tool complexity.

    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 simple 0-parameter auth utility without annotations or output schema, the description covers essential purpose and usage triggers. Could be improved by mentioning success/failure signals or timeout behavior, but adequately complete for selection purposes.

    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?

    Input schema has zero parameters. Per scoring rules, 0 params = baseline 4. The description appropriately contains no parameter discussion since none exist.

    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?

    Clear specific verb ('Open') and resource ('browser window to authenticate with Claude'). The description effectively distinguishes this from sibling get_claude_usage by positioning it as the auth recovery mechanism.

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

    Usage Guidelines5/5

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

    Excellent explicit guidance: 'Use this if get_claude_usage fails due to authentication.' States the exact trigger condition and identifies the sibling alternative, creating a clear decision tree for the agent.

    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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