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lucasliet

GitHub Copilot Usage MCP Server

by lucasliet
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JavaScript
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.1.5

  • Disambiguation4/5

    The three tools all retrieve GitHub Copilot usage data, but each targets a different output format (raw, human-readable, summary). While distinct, an agent might still be uncertain which to use for a given task, though descriptions clarify the differences.

    Naming Consistency5/5

    All tool names follow a consistent 'get_copilot_usage' prefix with suffixes that clearly indicate the output type (empty, _formatted, _summary). This pattern is predictable and easy to understand.

    Tool Count5/5

    With exactly 3 tools, the count is well-scoped for a focused server that provides usage information in three formats. It covers the necessary variations without being excessive or insufficient.

    Completeness4/5

    The surface covers the primary use case of retrieving Copilot usage data, offering different formats. Minor gaps exist, such as the absence of historical data or team-specific queries, but these are not essential for the stated purpose.

  • Average 3.3/5 across 3 of 3 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

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

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided; description mentions 'saves tokens' hinting at low cost but doesn't disclose auth requirements, side effects, or error scenarios. Limited behavioral context.

    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?

    Single sentence conveying purpose and key output information. Efficient for a simple tool, though language may be an issue for non-Portuguese agents.

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

    Completeness2/5

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

    No output schema and no annotations; description lacks details about return format, prerequisites, or edge cases. For a parameterless tool, it is sparse.

    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?

    No parameters in the schema; baseline score for zero-parameter tool as per calibration. Description adds no parameter info, but none needed.

    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?

    Clearly indicates it retrieves a concise summary of GitHub Copilot usage, mentioning specific information like premium quota. Differentiates from siblings (get_copilot_usage and get_copilot_usage_formatted) through 'concise summary'.

    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?

    No explicit guidance on when to use this tool versus the sibling tools. The description implies it is for a quick overview, but lacks direct comparison or context.

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

  • Behavior2/5

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

    No annotations exist, and the description does not disclose behavioral aspects such as read-only nature, rate limits, authentication requirements, or side effects. The verb 'gets' suggests a read operation, but this is not confirmed, leaving the agent with minimal behavioral insight.

    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, front-loaded sentence that efficiently conveys the core purpose. Every word is justified, and there is no extraneous information.

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

    Completeness2/5

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

    Given the lack of an output schema, the description should compensate by explaining the return format or content. The phrase 'humanized format' is vague and does not clarify what data is returned. The tool is simple, but the description remains incomplete.

    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 zero parameters, so the schema coverage is 100% by default. The description adds no parameter details, but none are needed. Per guidelines, 0 parameters yields a baseline of 4.

    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 clearly states that it retrieves GitHub Copilot usage information in a humanized format, specifying the verb 'gets' and the resource. However, it does not differentiate from sibling tools get_copilot_usage and get_copilot_usage_summary, which may overlap in function.

    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?

    No guidance is provided on when to use this tool versus alternatives like get_copilot_usage or get_copilot_usage_summary. The description implies a preference for human-readable output, but this is not explicitly stated as a usage condition.

    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?

    With no annotations, the description carries the burden. It describes a read operation returning usage data, which implies no side effects. However, it does not explicitly state it is read-only or mention authentication or rate limits. For a simple get tool, this is adequate but not thorough.

    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, clear sentence with no unnecessary words. It is front-loaded with the key purpose and includes specific details about the data type.

    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?

    Given the tool's simplicity (no parameters, no output schema), the description comprehensively explains the tool's purpose and return content. No additional information is needed for correct invocation.

    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 input schema has zero parameters and schema coverage is 100% vacuously. The description adds meaning by detailing what the tool returns (usage, quotas, limits, original API data), which goes beyond the empty schema.

    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 the tool retrieves GitHub Copilot usage information including quotas and limits, and specifies it returns original API data. This effectively differentiates it from siblings like get_copilot_usage_formatted and get_copilot_usage_summary.

    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?

    No guidance is provided on when to use this tool versus the sibling tools (formatted, summary). An agent would not know which one to select based on the description alone.

    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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