Interfaces with the GitHub API to retrieve Enterprise and Organization-level data, including seat assignments and integration summaries.
Enables retrieval of daily usage metrics, chat activity, active user counts, and detailed breakdowns by language and editor for GitHub Copilot at the Enterprise, Organization, and Team levels.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@copilot-usage-mcp Show me a summary of GitHub Copilot usage and seat assignments for my organization"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GitHub Copilot の利用状況メトリクスを取得する MCP (Model Context Protocol) サーバーです。
Enterprise、Organization、Team レベルでの Copilot 使用状況データを MCP ツールとして提供します。
ツール名 | 説明 |
| Enterprise 全体の日次 Copilot 利用メトリクス |
| Organization の日次 Copilot 利用メトリクス |
| Team の日次 Copilot 利用メトリクス |
| Organization の Copilot シート割り当て情報 |
| Enterprise / Org / Seats の統合サマリー |
各ツールは、コード補完数・Chat 利用状況・アクティブユーザー数・言語 / エディタ別の内訳などを取得できます。
Related MCP server: copilot-status-mcp
Node.js 18+
GitHub Personal Access Token（Copilot メトリクス API へのアクセス権限が必要）
npm install
npm run buildコマンドライン引数または環境変数で以下を設定してください（.env ファイルは読み込まれません）。
変数名 | 必須 | 説明 |
| Yes | GitHub Personal Access Token |
| No | Enterprise slug |
| No | Organization 名 |
| No | キャッシュディレクトリ（デフォルト: |
Claude Desktop の場合、claude_desktop_config.json に以下を追加します。
{
"mcpServers": {
"copilot-usage": {
"command": "node",
"args": ["path/to/copilot-usage-mcp/dist/index.js"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxxxxxxxxxxx",
"GITHUB_ENTERPRISE": "your-enterprise",
"GITHUB_ORG": "your-org"
}
}
}
}# TypeScript のウォッチモードで開発
npm run dev
# テスト実行
npm test
# テストのウォッチモード
npm run test:watchGet daily Copilot usage metrics for the entire GitHub Enterprise (code completions, chat usage, active users, language/editor breakdown)
| Name | Required | Description | Default |
|---|---|---|---|
| enterprise | No | Enterprise slug (defaults to GITHUB_ENTERPRISE env var) | |
| since | No | Start date in YYYY-MM-DD format (defaults to 28 days ago) | |
| until | No | End date in YYYY-MM-DD format (defaults to today) | |
| force_refresh | No | Ignore cache and fetch fresh data |
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 mentions 'daily' metrics and the types of data retrieved, which adds useful context. However, it lacks details on behavioral traits such as rate limits, authentication requirements, data freshness, or potential side effects like caching behavior implied by the force_refresh parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and includes all necessary details without redundancy. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides a clear purpose and scope but lacks details on return values, error handling, or operational constraints. It is adequate for a read-only tool but could benefit from more context on data format or usage limits to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the format or implications of the metrics breakdown. Baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get daily Copilot usage metrics') and resource ('for the entire GitHub Enterprise'), with explicit details on what metrics are included (code completions, chat usage, active users, language/editor breakdown). It distinguishes from sibling tools by specifying enterprise-level scope versus org/team-level alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates when to use this tool by specifying 'for the entire GitHub Enterprise,' which differentiates it from sibling tools like get_copilot_metrics_for_org or get_copilot_metrics_for_team. However, it does not explicitly state when not to use it or provide alternative scenarios beyond the scope distinction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get daily Copilot usage metrics for a GitHub Organization (code completions, chat usage, active users, language/editor breakdown)
| Name | Required | Description | Default |
|---|---|---|---|
| org | No | Organization name (defaults to GITHUB_ORG env var) | |
| since | No | Start date in YYYY-MM-DD format (defaults to 28 days ago) | |
| until | No | End date in YYYY-MM-DD format (defaults to today) | |
| force_refresh | No | Ignore cache and fetch fresh data |
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 mentions 'daily' metrics and the types of data (e.g., code completions, chat usage), which adds context about what the tool returns. However, it lacks details on behavioral traits such as authentication requirements, rate limits, error handling, or data freshness (beyond the force_refresh parameter).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and includes key details (daily metrics, specific breakdowns). There is no wasted language, and it effectively communicates the tool's function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides a clear purpose and some context (e.g., metric types). However, for a tool with 4 parameters and no structured output information, it lacks details on return values, error cases, or operational constraints, leaving gaps in completeness for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (org, since, until, force_refresh) with descriptions and defaults. The description does not add any additional meaning or clarification beyond what the schema provides, such as explaining relationships between parameters or usage nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get daily Copilot usage metrics') and the resource ('for a GitHub Organization'), specifying the exact metrics included (code completions, chat usage, active users, language/editor breakdown). It distinguishes itself from sibling tools by focusing on organization-level metrics rather than enterprise, team, seat assignments, or usage summary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'for a GitHub Organization,' which differentiates it from sibling tools like get_copilot_metrics_for_enterprise or get_copilot_metrics_for_team. However, it does not explicitly state when to use this tool versus those alternatives or provide any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get daily Copilot usage metrics for a specific team within an Organization
| Name | Required | Description | Default |
|---|---|---|---|
| org | No | Organization name (defaults to GITHUB_ORG env var) | |
| team_slug | Yes | Team slug (required) | |
| since | No | Start date in YYYY-MM-DD format (defaults to 28 days ago) | |
| until | No | End date in YYYY-MM-DD format (defaults to today) | |
| force_refresh | No | Ignore cache and fetch fresh data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does without disclosing behavioral traits. It doesn't mention permissions needed, rate limits, data freshness (beyond 'force_refresh' param), error handling, or output format, leaving significant gaps for a tool with 5 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral aspects, output structure, error conditions, and usage context relative to siblings, leaving the agent with incomplete information for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional semantic context beyond implying 'team' scope, which is already covered by the 'team_slug' parameter. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get daily Copilot usage metrics') and target resource ('for a specific team within an Organization'), making the purpose understandable. It distinguishes from sibling tools like 'get_copilot_metrics_for_enterprise' and 'get_copilot_metrics_for_org' by specifying 'team' scope, though it doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives like 'get_copilot_metrics_for_org' or 'get_copilot_usage_summary' is provided. The description implies usage for team-level metrics but lacks context on prerequisites, exclusions, or comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Copilot seat assignments for an Organization (user list with last activity date, editor info, plan type)
| Name | Required | Description | Default |
|---|---|---|---|
| org | No | Organization name (defaults to GITHUB_ORG env var) | |
| force_refresh | No | Ignore cache and fetch fresh data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but lacks behavioral details. It mentions caching via 'force_refresh' parameter, but doesn't disclose permissions required, rate limits, error handling, or data freshness implications. This is a significant gap for a tool that likely involves sensitive organizational data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and key return details. There is no wasted text, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain the return format (e.g., structure of the user list), error cases, or authentication needs, which are critical for a tool that fetches organizational data. The schema covers inputs well, but overall context is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('org' and 'force_refresh') adequately. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('Copilot seat assignments for an Organization'), specifying it returns a user list with last activity date, editor info, and plan type. It distinguishes from sibling tools by focusing on seat assignments rather than metrics or usage summaries, though it doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 (e.g., get_copilot_metrics_for_org). The description implies it's for retrieving seat assignments, but it doesn't specify scenarios, prerequisites, or alternatives, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a combined summary of Copilot usage across Enterprise, Organization, and seat assignments. Optionally include team-level metrics.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Start date in YYYY-MM-DD format (defaults to 28 days ago) | |
| until | No | End date in YYYY-MM-DD format (defaults to today) | |
| team_slug | No | Team slug to include team-level metrics | |
| force_refresh | No | Ignore cache and fetch fresh data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It describes the scope (combined summary across entities) and optional team metrics, but lacks details on permissions, rate limits, or response format. It adds some behavioral context but is incomplete for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the main purpose and includes an optional feature. There is no wasted text, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and scope but lacks details on behavioral traits like authentication or return values. It's adequate but has clear gaps for a tool with four parameters and no structured output information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description mentions 'optionally include team-level metrics,' which hints at the team_slug parameter but adds minimal semantic value beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'combined summary of Copilot usage across Enterprise, Organization, and seat assignments,' making the purpose specific. It distinguishes from sibling tools by emphasizing the combined nature versus the individual metrics tools like get_copilot_metrics_for_enterprise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool—for a combined summary rather than individual metrics—and mentions an optional feature for team-level metrics. However, it does not explicitly state when not to use it or name alternatives, though siblings are implied by context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose targeting different aspects of Copilot usage data. The enterprise, organization, team, seat assignments, and summary tools all operate at different scopes with no functional overlap, making tool selection unambiguous.
All tools follow a perfect 'get_copilot_[noun]_[preposition]_[target]' pattern with consistent snake_case throughout. The naming convention is highly predictable and readable across all five tools.
Five tools is an ideal number for this focused domain of Copilot usage analytics. Each tool serves a distinct reporting function without redundancy, providing comprehensive coverage while maintaining a manageable interface.
The toolset provides complete coverage for Copilot usage reporting across all relevant scopes (enterprise, organization, team, seat assignments) with a convenient summary tool. There are no obvious gaps in the analytics surface for this specific domain.
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