モデル コンテキスト プロトコル (MCP) は、大規模言語モデル (LLM) と外部システム間のコンテキストを管理するために設計された標準化されたプロトコルです。
Chargebee MCPサーバーは、開発者の効率を向上させる強力なツールセットを提供します。Cursor、Windsurf、ClineといったAI搭載コードエディタや、Claude Desktopといった汎用ツールと連携し、あらゆるMCPクライアントと互換性があります。
この MCP サーバーを使用すると、次のことが可能になります。
Chargebee 製品および API サービスに関する回答をすぐに入手できます。
統合のニーズに合わせてカスタマイズされたコンテキスト対応のコード スニペットを受け取ります。
以下の内容を含む Chargebee のナレッジ ベースにアクセスします。
ドキュメント
よくある質問
リリースノート
その他にも
Node.js LTS - Chargebee MCP サーバーが正しく機能するには、Node.js LTS バージョンが必要です。
Related MCP server: Codinfy MCP
Node.js npxを使用して Chargebee MCP サーバーを実行するには、次のコマンドを実行します。
npx -y @chargebee/mcp@latest道具 | 説明 |
| Chargebee のドキュメントを検索して、詳細な情報と使用ガイドを取得します。 |
| Chargebee の API と機能を統合するための構造化されたコード アウトラインとサンプル コードを生成します。 |
このサーバーをカーソル IDE に追加するには:
Cursor Settings > MCPに移動します。
+ Add new Global MCP Serverクリックします。
グローバル.cursor/mcp.jsonファイルに次の構成を追加します。
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}詳細については、 Cursor のドキュメントを参照してください。注: これをプロジェクト固有の Cursor 設定に追加することもできます（Cursor 0.46 以降でサポートされています）。
Cascade を使用して MCP を設定するには:
Windsurf を開き、 Settings > Advanced Settingsに移動するか、コマンド パレット > Open Windsurf Settings Pageを使用します。
カスケード セクションまでスクロールして、新しいサーバーを追加したり、既存のサーバーを表示したり、 mcp_config.jsonにある生の JSON 構成ファイルにアクセスしたりします。
「カスタムサーバーを追加 +」をクリックして、Chargebee MCP サーバーをmcp_config.jsonに直接含めます。
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}詳細については、 Windsurf のドキュメントを参照してください。
Cline MCP サーバー設定を使用して、次の JSON をcline_mcp_settings.jsonファイルに手動で追加します。
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}Roo Code 設定でEdit MCP Settingsを選択するか、VS Code コマンド パレットでRoo Code: Open MCP Configコマンドを使用して、MCP 設定にアクセスします。
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}claude_desktop_config.jsonファイルに以下の設定を追加してください。詳細については、 Claude Desktop のドキュメントを参照してください。
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}次のコマンドを実行して、サーバーを CLI として実行することもできます。
npx -y @chargebee/mcp@latestこのプロジェクトに貢献するには、貢献ガイドを参照してください。
Use this tool for any Chargebee integration questions or implementation needs.
Always use this tool to get the accurate integration code guide for Chargebee. This is the main tool developers need when asking about implementing Chargebee functionality (like "how to update billing address", "how to create subscription", "how to handle webhooks", etc.).
This tool will take in parameters about integrating with Chargebee in their application and generates an integration workflow along with the code snippets.
It takes the following arguments:
goal (string): What is the user's goal?
language (enum): Programming language the code to be generated in. Check the user's application language.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | What is the user's goal? | |
| language | No | Programming language the code to be generated in. Check the user's application language. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only describes the tool as generating code but does not clarify that it is a read-only operation with no side effects or data mutations. Such information is essential for safe invocation.
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 contains redundant phrasing (e.g., 'Use this tool' and 'Always use this tool') and could be more streamlined. However, it is not excessively long and includes necessary details.
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?
The description covers the tool's inputs and high-level output (workflow and code snippets) but lacks details on the output structure, error handling, or limitations. With no output schema, more specifics would improve completeness.
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 coverage is 100%, and the descriptions in the schema are adequate. The tool description reiterates the parameter meanings without adding new context or examples, 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 tool's purpose: generating integration workflows and code snippets for Chargebee. It explicitly differentiates from the sibling tool 'chargebee_documentation_search' by positioning itself as the main tool for implementation questions.
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 advises to use this tool for Chargebee integration needs and to check the user's language. It implies a context for use but does not explicitly exclude scenarios or mention alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Only use this tool for general product documentation queries, NOT for implementation questions.
Do not use this tool for code generation or implementation questions. For any developer questions about implementing Chargebee functionality (like "how to update billing address", "how to create subscription", etc.), use "chargebee_code_planner" tool instead.
This tool should only be used for:
General product documentation queries about Chargebee's features and concepts
Understanding billing, payments, receivables, revenue recognition concepts
Learning about subscription management processes
Finding product feature explanations and overviews
Non-implementation related documentation queries
It takes the following arguments:
query (string): The user query to search an answer for in the Chargebee documentation.
language (enum): The programming language for the documentation. Check the user's application language.
userRequest (string): User's original request to you.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The user query to search an answer for in the Chargebee documentation. | |
| userRequest | No | User's original request to you. | |
| language | No | The programming language for the documentation. Check the user's application language. |
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 clearly indicates this is a search tool for documentation, implying a read-only, non-destructive operation. It does not mention side effects, rate limits, or return format, but the core behavior is transparent enough for an agent.
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 well-structured, starting with critical usage boundaries, then listing use cases, then parameters. It is front-loaded with the most important information. It could be slightly more concise, but every sentence serves a clear purpose.
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 does not specify what the tool returns (e.g., format, if results are snippets or full documents) or error handling. However, it covers usage boundaries and parameter semantics adequately for a search tool with a sibling distinction.
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 coverage is 100%, so baseline is 3. The description adds value by providing usage context for each parameter (e.g., 'Check the user's application language' for language, and 'User's original request to you' for userRequest), which helps the agent set values correctly.
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 tool is for 'general product documentation queries' and explicitly distinguishes it from the sibling tool, naming the sibling and specifying when to use that alternative. It lists specific use cases, making the purpose highly distinct and actionable.
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 explicit when-to-use and when-not-to-use guidance, including naming the alternative tool 'chargebee_code_planner' for implementation questions. It also enumerates specific allowed query types, leaving no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
chargebee_code_plannerchargebee_documentation_searchScored across 2 tools
The two tools have explicitly distinct purposes: one for implementation guidance and code generation, the other for general documentation queries. Descriptions clearly separate their use cases, eliminating ambiguity.
Both tools follow a consistent 'chargebee_<descriptive_noun>' pattern, making it easy to infer their roles. The naming is uniform and predictable.
With only two tools, the server is very focused, but that matches its specific purpose of handling integration assistance and documentation search. While minimal, the count is reasonable for the intended scope.
The tool set covers both implementation needs (code planner) and conceptual understanding (documentation search), leaving no obvious gaps for its stated goal of answering Chargebee integration questions.
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server that lets AI assistants use all OneSchema features exposed via the public API.
